Algorithms for Deterministic Incremental - Ricerca sull'intelligenza artificiale specializzata al MIT

Algorithms for Deterministic Incremental
Dependency Parsing

Joakim Nivre∗,∗∗
V¨axj ¨o University, Uppsala University

Parsing algorithms that process the input from left to right and construct a single derivation
have often been considered inadequate for natural language parsing because of the massive
ambiguity typically found in natural language grammars. Nevertheless, it has been shown
that such algorithms, combined with treebank-induced classiﬁers, can be used to build highly
accurate disambiguating parsers, in particular for dependency-based syntactic representations.
In questo articolo, we ﬁrst present a general framework for describing and analyzing algorithms
for deterministic incremental dependency parsing, formalized as transition systems. We then
describe and analyze two families of such algorithms: stack-based and list-based algorithms.
In the former family, which is restricted to projective dependency structures, we describe an
arc-eager and an arc-standard variant; in the latter family, we present a projective and a non-
projective variant. For each of the four algorithms, we give proofs of correctness and complexity.
Inoltre, we perform an experimental evaluation of all algorithms in combination with
SVM classiﬁers for predicting the next parsing action, using data from thirteen languages. Noi
show that all four algorithms give competitive accuracy, although the non-projective list-based
algorithm generally outperforms the projective algorithms for languages with a non-negligible
proportion of non-projective constructions. Tuttavia, the projective algorithms often produce
comparable results when combined with the technique known as pseudo-projective parsing. IL
linear time complexity of the stack-based algorithms gives them an advantage with respect to
efﬁciency both in learning and in parsing, but the projective list-based algorithm turns out to
be equally efﬁcient in practice. Inoltre, when the projective algorithms are used to implement
pseudo-projective parsing, they sometimes become less efﬁcient in parsing (but not in learning)
than the non-projective list-based algorithm. Although most of the algorithms have been partially
described in the literature before, this is the ﬁrst comprehensive analysis and evaluation of the
algorithms within a uniﬁed framework.

1. introduzione

Because parsers for natural language have to cope with a high degree of ambigu-
ity and nondeterminism, they are typically based on different techniques than the
ones used for parsing well-deﬁned formal languages—for example, in compilers for

∗ School of Mathematics and Systems Engineering, V¨axj ¨o University, 35195 V¨axj ¨o, Sweden.

E-mail: joakim.nivre@vxu.se.

∗∗ Department of Linguistics and Philology, Uppsala University, Box 635, 75126 Uppsala, Sweden.

E-mail: joakim.nivre@lingﬁl.uu.se.

Invio ricevuto: 29 May 2007; revised submission received 22 settembre 2007; accepted for publication:
3 novembre 2007.

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Linguistica computazionale

Volume 34, Numero 4

programming languages. Così, the mainstream approach to natural language parsing
uses algorithms that efﬁciently derive a potentially very large set of analyses in parallel,
typically making use of dynamic programming and well-formed substring tables or
charts. When disambiguation is required, this approach can be coupled with a statistical
model for parse selection that ranks competing analyses with respect to plausibility.
Although it is often necessary, for efﬁciency reasons, to prune the search space prior
to the ranking of complete analyses, this type of parser always has to handle multiple
analyses.

By contrast, parsers for formal languages are usually based on deterministic parsing
techniques, which are maximally efﬁcient in that they only derive one analysis. Questo
is possible because the formal language can be deﬁned by a non-ambiguous formal
grammar that assigns a single canonical derivation to each string in the language, UN
property that cannot be maintained for any realistically sized natural language gram-
mar. Consequently, these deterministic parsing techniques have been much less popular
for natural language parsing, except as a way of modeling human sentence process-
ing, which appears to be at least partly deterministic in nature (Marcus 1980; Shieber
1983).

More recently, Tuttavia, it has been shown that accurate syntactic disambiguation
for natural language can be achieved using a pseudo-deterministic approach, Dove
treebank-induced classiﬁers are used to predict the optimal next derivation step when
faced with a nondeterministic choice between several possible actions. Compared to
the more traditional methods for natural language parsing, this can be seen as a severe
form of pruning, where parse selection is performed incrementally so that only a single
analysis is derived by the parser. This has the advantage of making the parsing process
very simple and efﬁcient but the potential disadvantage that overall accuracy suffers
because of the early commitment enforced by the greedy search strategy. Somewhat
surprisingly, Anche se, research has shown that, with the right choice of parsing algorithm
and classiﬁer, this type of parser can achieve state-of-the-art accuracy, especially when
used with dependency-based syntactic representations.

Classiﬁer-based dependency parsing was pioneered by Kudo and Matsumoto
(2002) for unlabeled dependency parsing of Japanese with head-ﬁnal dependencies
only. The algorithm was generalized to allow both head-ﬁnal and head-initial depen-
dencies by Yamada and Matsumoto (2003), who reported very good parsing accuracy
for English, using dependency structures extracted from the Penn Treebank for training
and testing. The approach was extended to labeled dependency parsing by Nivre, Hall,
and Nilsson (2004) (for Swedish) and Nivre and Scholz (2004) (for English), using a
different parsing algorithm ﬁrst presented in Nivre (2003). At a recent evaluation of
data-driven systems for dependency parsing with data from 13 different languages
(Buchholz and Marsi 2006), the deterministic classiﬁer-based parser of Nivre et al. (2006)
reached top performance together with the system of McDonald, Lerman, and Pereira
(2006), which is based on a global discriminative model with online learning. These
results indicate that, at least for dependency parsing, deterministic parsing is possible
without a drastic loss in accuracy. The deterministic classiﬁer-based approach has also
been applied to phrase structure parsing (Kalt 2004; Sagae and Lavie 2005), although the
accuracy for this type of representation remains a bit below the state of the art. In this
setting, more competitive results have been achieved using probabilistic classiﬁers and
beam search, rather than strictly deterministic search, as in the work by Ratnaparkhi
(1997, 1999) and Sagae and Lavie (2006).

A deterministic classiﬁer-based parser consists of three essential components: UN
parsing algorithm, which deﬁnes the derivation of a syntactic analysis as a sequence

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Deterministic Incremental Dependency Parsing

of elementary parsing actions; a feature model, which deﬁnes a feature vector represen-
tation of the parser state at any given time; and a classiﬁer, which maps parser states,
as represented by the feature model, to parsing actions. Although different types of
parsing algorithms, feature models, and classiﬁers have been used for deterministic
dependency parsing, there are very few studies that compare the impact of different
components. The notable exceptions are Cheng, Asahara, and Matsumoto (2005), who
compare two different algorithms and two types of classiﬁer for parsing Chinese, E
Hall, Nivre, and Nilsson (2006), who compare two types of classiﬁers and several types
of feature models for parsing Chinese, English, and Swedish.

In questo articolo, we focus on parsing algorithms. More precisely, we describe two
families of algorithms that can be used for deterministic dependency parsing, supported
by classiﬁers for predicting the next parsing action. The ﬁrst family uses a stack to store
partially processed tokens and is restricted to the derivation of projective dependency
structures. The algorithms of Kudo and Matsumoto (2002), Yamada and Matsumoto
(2003), and Nivre (2003, 2006B) all belong to this family. The second family, represented
by the algorithms described by Covington (2001) and recently explored for classiﬁer-
based parsing in Nivre (2007), instead uses open lists for partially processed tokens,
which allows arbitrary dependency structures to be processed (in particular, structures
with non-projective dependencies). We provide a detailed analysis of four different
algorithms, two from each family, and give proofs of correctness and complexity for
each algorithm. Inoltre, we perform an experimental evaluation of accuracy and
efﬁciency for the four algorithms, combined with state-of-the-art classiﬁers, using data
from 13 different languages. Although variants of these algorithms have been partially
described in the literature before, this is the ﬁrst comprehensive analysis and evaluation
of the algorithms within a uniﬁed framework.

The remainder of the article is structured as follows. Sezione 2 deﬁnes the task of
dependency parsing and Section 3 presents a formal framework for the characterization
of deterministic incremental parsing algorithms. Sezioni 4 E 5 contain the formal
analysis of four different algorithms, deﬁned within the formal framework, with proofs
of correctness and complexity. Sezione 6 presents the experimental evaluation; Sezione 7
reports on related work; and Section 8 contains our main conclusions.

2. Dependency Parsing

Dependency-based syntactic theories are based on the idea that syntactic structure can
be analyzed in terms of binary, asymmetric dependency relations holding between the
words of a sentence. This basic conception of syntactic structure underlies a variety of
different linguistic theories, such as Structural Syntax (Tesni`ere 1959), Functional Gener-
ative Description (Sgall, Hajiˇcov´a, and Panevov´a 1986), Meaning-Text Theory (Mel’ˇcuk
1988), and Word Grammar (Hudson 1990). In computational linguistics, dependency-
based syntactic representations have in recent years been used primarily in data-driven
models, which learn to produce dependency structures for sentences solely from an
annotated corpus, as in the work of Eisner (1996), Yamada and Matsumoto (2003), Nivre,
Hall, and Nilsson (2004), and McDonald, Crammer, and Pereira (2005), among others.
One potential advantage of such models is that they are easily ported to any domain or
language in which annotated resources exist.

In this kind of framework the syntactic structure of a sentence is modeled by a depen-
dency graph, which represents each word and its syntactic dependents through labeled
directed arcs. This is exempliﬁed in Figure 1, for a Czech sentence taken from the Prague

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Linguistica computazionale

Volume 34, Numero 4

✞

ROOT0

✞

AuxP

AuxK

✞
✞

(cid:2)

AuxP

(cid:2)

Pred
Atr

✞
❄
Z1
(Out-of

(cid:2)

AuxZ

(cid:2)
❄
nich2
them

✞
❄
❄
jen4
jedna5
only
one-FEM-SG
(“Only one of them concerns quality.”)

❄
je3
È

(cid:2)

✞
❄
na6
A

Adv

(cid:2)
❄
kvalitu7
quality

Figura 1
Dependency graph for a Czech sentence from the Prague Dependency Treebank.

✞

ROOT

✞
❄

NMOD

SBJ

(cid:2)

✞
❄
news2

ROOT0 Economic1

✞
✞

(cid:2)

❄
had3

OBJ

(cid:2)

✞

PMOD

(cid:2)

✞
NMOD
❄
little4

✞ (cid:2)
NMOD
❄
❄
on6
effect5

✞
❄
ﬁnancial7

NMOD

markets8

(cid:2)

❄

Figura 2
Dependency graph for an English sentence from the Penn Treebank.

(cid:2)

❄
.8
.)

(cid:2)

❄
.9

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Dependency Treebank (Hajiˇc et al. 2001; B ¨ohmov´a et al. 2003), and in Figure 2, for an
English sentence taken from the Penn Treebank (Marcus, Santorini, and Marcinkiewicz
1993; Marcus et al. 1994).1 An artiﬁcial word ROOT has been inserted at the beginning
of each sentence, serving as the unique root of the graph. This is a standard device that
simpliﬁes both theoretical deﬁnitions and computational implementations.

Deﬁnition 1
Given a set L = {l1, . . . , l|l|} of dependency labels, a dependency graph for a sentence
x = (w0, w1, . . . , wn) is a labeled directed graph G = (V, UN), Dove

V = {0, 1, . . . , N} is a set of nodes,
A ⊆ V × L × V is a set of labeled directed arcs.

The set V of nodes (or vertices) is the set of non-negative integers up to and including
N, each corresponding to the linear position of a word in the sentence (including ROOT).
The set A of arcs (or directed edges) is a set of ordered triples (io, l, j), where i and j
are nodes and l is a dependency label. Because arcs are used to represent dependency
relations, we will say that i is the head and l is the dependency type of j. Conversely,
we say that j is a dependent of i.

1 In the latter case, the dependency graph has been derived automatically from the constituency-based

annotation in the treebank, using the Penn2Malt program, available at http://w3.msi.vxu.se/users/
nivre/research/Penn2Malt.html.

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Nivre

Deterministic Incremental Dependency Parsing

Deﬁnition 2
A dependency graph G = (V, UN) is well-formed if and only if:

The node 0 is a root, questo è, there is no node i and label l such that
(io, l, 0) ∈ A.
Every node has at most one head and one label, questo è, if (io, l, j) ∈ A then
there is no node i(cid:2) and label l(cid:2) such that (io(cid:2), l(cid:2), j) ∈ A and i (cid:7)= i(cid:2) or l (cid:7)= l(cid:2).
The graph G is acyclic, questo è, Non c'è (non-empty) subset of arcs
{(i0, l1, i1), (i1, l2, i2), . . . , (ik−1, lk, ik)} ⊆ A such that i0 = ik.

We will refer to conditions 1–3 as ROOT, SINGLE-HEAD, and ACYCLICITY, rispettivamente.
Any dependency graph satisfying these conditions is a dependency forest; if it is also
connected, it is a dependency tree, questo è, a directed tree rooted at the node 0. It is worth
noting that any dependency forest can be turned into a dependency tree by adding arcs
from the node 0 to all other roots.

Deﬁnition 3
A dependency graph G = (V, UN) is projective if and only if, for every arc (io, l, j) ∈
A and node k ∈ V, if i < k < j or j < k < i then there is a subset of arcs {(i, l1, i1), (i1, l2, i2), . . . (ik−1, lk, ik)} ∈ A such that ik = k. In a projective dependency graph, every node has a continuous projection, where the projection of a node i is the set of nodes reachable from i in the reﬂexive and transitive closure of the arc relation. This corresponds to the ban on discontinuous constituents in orthodox phrase structure representations. We call this condition PROJECTIVITY. When discussing PROJECTIVITY, we will often use the notation i →∗ j to mean that j is reachable from i in the reﬂexive and transitive closure of the arc relation. Example 1 For the graphs depicted in Figures 1 and 2, we have: Figure 1: G1 V1 A1 (V1, A1) = = {0, 1, 2, 3, 4, 5, 6, 7, 8} = {(0, Pred, 3), (0, AuxK, 8), (1, Atr, 2), (3, Sb, 5), (3, AuxP, 6), (5, AuxP, 1), (5, AuxZ, 4), (6, Adv, 7)} Figure 2: G2 V2 A2 (V2, A2) = = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9} = {(0, ROOT, 3), (2, NMOD, 1), (3, SBJ, 2), (3, OBJ, 5), (3, P, 9), (5, NMOD, 4), (5, NMOD, 6), (6, PMOD, 8), (8, NMOD, 7)} Both G1 and G2 are well-formed dependency forests (dependency trees, to be speciﬁc), but only G2 is projective. In G1, the arc (5, AuxP, 1) spans node 3, which is not reachable from node 5 by following dependency arcs. 3. Deterministic Incremental Dependency Parsing In this section, we introduce a formal framework for the speciﬁcation of deterministic dependency parsing algorithms in terms of two components: a transition system, which 517 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Computational Linguistics Volume 34, Number 4 is nondeterministic in the general case, and an oracle, which always picks a single transition out of every parser conﬁguration. The use of transition systems to study computation is a standard technique in theoretical computer science, which is here combined with the notion of oracles in order to characterize parsing algorithms with deterministic search. In data-driven dependency parsing, oracles normally take the form of classiﬁers, trained on treebank data, but they can also be deﬁned in terms of grammars and heuristic disambiguation rules (Nivre 2003). The main reason for introducing this framework is to allow us to characterize algorithms that have previously been described in different traditions and to compare their formal properties within a single uniﬁed framework. In particular, whereas this type of framework has previously been used to characterize algorithms in the stack- based family (Nivre 2003, 2006b; Attardi 2006), it is here being used also for the list- based algorithms ﬁrst discussed by Covington (2001). Deﬁnition 4 A transition system for dependency parsing is a quadruple S = (C, T, cs, Ct), where 1. 2. 3. 4. C is a set of conﬁgurations, each of which contains a buffer β of (remaining) nodes and a set A of dependency arcs, T is a set of transitions, each of which is a (partial) function t : C → C, cs is an initialization function, mapping a sentence x = (w0, w1, . . . , wn) to a conﬁguration with β = [1, . . . , n], Ct ⊆ C is a set of terminal conﬁgurations. A conﬁguration is required to contain at least a buffer β, initially containing the nodes [1, . . . , n] corresponding to the real words of a sentence x = (w0, w1, . . . , wn), and a set A of dependency arcs, deﬁned on the nodes in V = {0, 1, . . . , n}, given some set of dependency labels L. The speciﬁc transition systems deﬁned in Sections 4 and 5 will extend this basic notion of conﬁguration with different data structures, such as stacks and lists. We use the notation βc and Ac to refer to the value of β and A, respectively, in a conﬁguration c; we also use |β| to refer to the length of β (i.e., the number of nodes in the buffer) and we use [ ] to denote an empty buffer. Deﬁnition 5 Let S = (C, T, cs, Ct) be a transition system. A transition sequence for a sentence x = (w0, w1, . . . , wn) in S is a sequence C0,m = (c0, c1, . . . , cm) of conﬁgurations, such that 1. 2. 3. c0 = cs(x), cm ∈ Ct, for every i (1 ≤ i ≤ m), ci = t(ci−1) for some t ∈ T. The parse assigned to x by C0,m is the dependency graph Gcm = ({0, 1, . . . , n}, Acm ), where Acm is the set of dependency arcs in cm. Starting from the initial conﬁguration for the sentence to be parsed, transitions will manipulate β and A (and other available data structures) until a terminal conﬁguration is reached. Because the node set V is given by the input sentence itself, the set Acm of dependency arcs in the terminal conﬁguration will determine the output dependency graph Gcm = (V, Acm ). 518 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Nivre Deterministic Incremental Dependency Parsing Deﬁnition 6 A transition system S = (C, T, cs, Ct) is incremental if and only if, for every conﬁguration c ∈ C and transition t ∈ T, it holds that: 1. 2. 3. if βc = [ ] then c ∈ Ct, |βc| ≥ |βt(c)|, if a ∈ Ac then a ∈ At(c). The ﬁrst two conditions state that the buffer β never grows in size and that parsing terminates as soon as it becomes empty; the third condition states that arcs added to A can never be removed. Note that this is only one of several possible notions of incrementality in parsing. A weaker notion would be to only require that the set of arcs is built monotonically (the third condition); a stronger notion would be to require also that nodes in β are processed strictly left to right. Deﬁnition 7 Let S = (C, T, cs, Ct) be a transition system for dependency parsing. 1. 2. 3. S is sound for a class G of dependency graphs if and only if, for every sentence x and every transition sequence C0,m for x in S, the parse Gcm S is complete for a class G of dependency graphs if and only if, for every sentence x and every dependency graph Gx for x in G, there is a transition sequence C0,m for x in S such that Gcm = Gx. S is correct for a class G of dependency graphs if and only if it is sound and complete for G. ∈ G. The notions of soundness and completeness, as deﬁned here, can be seen as correspond- ing to the notions of soundness and completeness for grammar parsing algorithms, according to which an algorithm is sound if it only derives parses licensed by the grammar and complete if it derives all such parses (Shieber, Schabes, and Pereira 1995). Depending on the nature of a transition system S, there may not be a transition sequence for every sentence, or there may be more than one such sequence. The systems deﬁned in Sections 4 and 5 will all be such that, for any input sentence x = (w0, w1, . . . , wn), there is always at least one transition sequence for x (and usually more than one). Deﬁnition 8 An oracle for a transition system S = (C, T, cs, Ct) is a function o : C → T. Given a transition system S = (C, T, cs, Ct) and an oracle o, deterministic parsing can be achieved by the following simple algorithm: PARSE(x = (w0, w1, . . . , wn)) c ← cs(x) 1 2 while c (cid:7)∈ Ct 3 4 c ← [o(c)](c) return Gc 519 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Computational Linguistics Volume 34, Number 4 It is easy to see that, provided that there is at least one transition sequence in S for every sentence, such a parser constructs exactly one transition sequence C0,m for a sentence x and returns the parse deﬁned by the terminal conﬁguration cm, that is, Gcm = ({0, 1, . . . , n}, Acm ). The reason for separating the oracle o, which maps a conﬁgu- ration c to a transition t, from the transition t itself, which maps a conﬁguration c to a new conﬁguration c(cid:2), is to have a clear separation between the abstract machine deﬁned by the transition system, which determines formal properties such as correctness and complexity, and the search mechanism used when executing the machine. In the experimental evaluation in Section 6, we will use the standard technique of approximating oracles with classiﬁers trained on treebank data. However, in the formal characterization of different parsing algorithms in Sections 4 and 5, we will concentrate on properties of the underlying transition systems. In particular, assuming that both o(c) and t(c) can be performed in constant time (for every o, t and c), which is reasonable in most cases, the worst-case time complexity of a deterministic parser based on a transition system S is given by an upper bound on the length of transition sequences in S. And the space complexity is given by an upper bound on the size of a conﬁguration c ∈ C, because only one conﬁguration needs to be stored at any given time in a deterministic parser. 4. Stack-Based Algorithms The stack-based algorithms make use of a stack to store partially processed tokens, that is, tokens that have been removed from the input buffer but which are still considered as potential candidates for dependency links, either as heads or as dependents. A parser conﬁguration is therefore deﬁned as a triple, consisting of a stack, an input buffer, and a set of dependency arcs. Deﬁnition 9 A stack-based conﬁguration for a sentence x = (w0, w1, . . . , wn) is a triple c = (σ, β, A), where 1. 2. 3. σ is a stack of tokens i ≤ k (for some k ≤ n), β is a buffer of tokens j > k,
A is a set of dependency arcs such that G = ({0, 1, . . . , N}, UN) is a
dependency graph for x.

Both the stack and the buffer will be represented as lists, although the stack will have its
head (or top) to the right for reasons of perspicuity. Così, P|i represents a stack with top
i and tail σ, and j|β represents a buffer with head j and tail β.2 We use square brackets
for enumerated lists, Per esempio, [1, 2, . . . , N], con [ ] for the empty list as a special case.

Deﬁnition 10
A stack-based transition system is a quadruple S = (C, T, cs, Ct), Dove

C is the set of all stack-based conﬁgurations,
cs(x = (w0, w1, . . . wn)) = ([0], [1, . . . , N], ∅),

2 The operator | is taken to be left-associative for the stack and right-associative for the buffer.

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Deterministic Incremental Dependency Parsing

Transitions

LEFT-ARCl
RIGHT-ARCs
l

SHIFT

Preconditions

LEFT-ARCl

RIGHT-ARCs
l

(P|io, j|β, UN) ⇒ (P, j|β, A∪{(j, l, io)})
(P|io, j|β, UN) ⇒ (P, io|β, A∪{(io, l, j)})
(P, io|β, UN) ⇒ (P|io, β, UN)

¬[i = 0]
¬∃k∃l(cid:2)[(k, l(cid:2), io) ∈ A]
¬∃k∃l(cid:2)[(k, l(cid:2), j) ∈ A]

Figura 3
Transitions for the arc-standard, stack-based parsing algorithm.

T is a set of transitions, each of which is a function t : C → C,
Ct = {c ∈ C|c = (P, [ ], UN)}.

A stack-based parse of a sentence x = (w0, w1, . . . , wn) starts with the artiﬁcial root node
0 on the stack σ, all the nodes corresponding to real words in the buffer β, and an
empty set A of dependency arcs; it ends as soon as the buffer β is empty. The transitions
used by stack-based parsers are essentially composed of two types of actions: adding
(labeled) arcs to A and manipulating the stack σ and input buffer β. By combining such
actions in different ways, we can construct transition systems that implement different
parsing strategies. We will now deﬁne two such systems, which we call arc-standard
and arc-eager, rispettivamente, adopting the terminology of Abney and Johnson (1991).

4.1 Arc-Standard Parsing

The transition set T for the arc-standard, stack-based parser is deﬁned in Figure 3 E
contains three types of transitions:

Transitions LEFT-ARCl (for any dependency label l) add a dependency arc
(j, l, io) to A, where i is the node on top of the stack σ and j is the ﬁrst node in
the buffer β. Inoltre, they pop the stack σ. They have as a precondition
that the token i is not the artiﬁcial root node 0 and does not already have
a head.

Transitions RIGHT-ARCs
l (for any dependency label l) add a dependency
arc (io, l, j) to A, where i is the node on top of the stack σ and j is the ﬁrst
node in the buffer β. Inoltre, they pop the stack σ and replace j by i
at the head of β. They have as a precondition that the token j does not
already have a head.3

The transition SHIFT removes the ﬁrst node i in the buffer β and pushes
it on top of the stack σ.

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3 The superscript s is used to distinguish these transitions from the non-equivalent RIGHT-ARCe

l transitions

in the arc-eager system.

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Volume 34, Numero 4

Transition Conﬁguration
(
SHIFT =⇒ (
LEFT-ARCNMOD =⇒ (
SHIFT =⇒ (
LEFT-ARCSBJ =⇒ (
SHIFT =⇒ (
SHIFT =⇒ (
LEFT-ARCNMOD =⇒ (
SHIFT =⇒ (
SHIFT =⇒ (
SHIFT =⇒ (
LEFT-ARCNMOD =⇒ (
PMOD =⇒ (
RIGHT-ARCs
NMOD =⇒ (
RIGHT-ARCs
OBJ =⇒ (
SHIFT =⇒ (
P =⇒ (
ROOT =⇒ (
SHIFT =⇒ (

[0],
[0, 1],
[0],
[0, 2],
[0],
[0, 3],
[0, 3, 4],
[0, 3],
[0, 3, 5],
[0, . . . 6],
[0, . . . , 7],
[0, . . . 6],
[0, 3, 5],
[0, 3],
[0],
[0, 3],
[0],
[ ],
[0],

RIGHT-ARCs

∅
∅

[1, . . . , 9],
[2, . . . , 9],
[2, . . . , 9], A1 = {(2, NMOD, 1)}
[3, . . . , 9], A1
[3, . . . , 9], A2 = A1 ∪{(3, SBJ, 2)}
[4, . . . , 9], A2
[5, . . . , 9], A2
[5, . . . , 9], A3 = A2 ∪{(5, NMOD, 4)}
[6, . . . , 9], A3
A3
[7, 8, 9],
A3
[8, 9],
A4 = A3 ∪{(8, NMOD, 7)}
[8, 9],
A5 = A4 ∪{(6, PMOD, 8)}
[6, 9],
A6 = A5 ∪{(5, NMOD, 6)}
[5, 9],
A7 = A6 ∪{(3, OBJ, 5)}
[3, 9],
A7
[9],
A8 = A7 ∪{(3, P, 9)}
[3],
A9 = A8 ∪{(0, ROOT, 3)}
[0],
A9
[ ],

)
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)

Figura 4
Arc-standard transition sequence for the English sentence in Figure 2.

The arc-standard parser is the closest correspondent to the familiar shift-reduce parser
for context-free grammars (Aho, Sethi, and Ullman 1986). The LEFT-ARCl and RIGHT-
ARCs
l transitions correspond to reduce actions, replacing a head-dependent structure
with its head, whereas the SHIFT transition is exactly the same as the shift action. One
peculiarity of the transitions, as deﬁned here, is that the “reduce” transitions apply to
one node on the stack and one node in the buffer, rather than two nodes on the stack.
The reason for this formulation is to facilitate comparison with the arc-eager parser
described in the next section and to simplify the deﬁnition of terminal conﬁgurations.
By way of example, Figura 4 shows the transition sequence needed to parse the English
sentence in Figure 2.

Theorem 1
The arc-standard, stack-based algorithm is correct for the class of projective dependency
forests.

Proof 1
To show the soundness of the algorithm, we show that the dependency graph deﬁned
by the initial conﬁguration, Gcs(X) = (Vx, ∅), is a projective dependency forest, and that
every transition preserves this property. We consider each of the relevant conditions in
turn, keeping in mind that the only transitions that modify the graph are LEFT-ARCl
and RIGHT-ARCs
l .

ROOT: The node 0 is a root in Gcs(X), and adding an arc of the form (io, l, 0) È
prevented by an explicit precondition of LEFT-ARCl.

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SINGLE-HEAD: Every node i ∈ Vx has in-degree 0 in Gcs(X), and both
LEFT-ARCl and RIGHT-ARCs
of the new arc has in-degree 0.

l have as a precondition that the dependent

ACYCLICITY: Gcs(X) is acyclic, and adding an arc (io, l, j) will create a cycle
only if there is a directed path from j to i. But this would require that a
previous transition had added an arc of the form (k, l(cid:2), io) (for some k, l(cid:2)),
in which case i would no longer be in σ or β.

PROJECTIVITY: Gcs(X) is projective, and adding an arc (io, l, j) will make the
graph non-projective only if there is a node k such that i < k < j or j < k < i and neither i →∗ k nor j →∗ k. Let C0,m be a conﬁguration sequence for x = (w0, w1, . . . , wn) and let Π(p, i, j) (for 0 < p < m, 0 ≤ i < j ≤ n) be the claim that, for every k such that i < k < j, i →∗ k or j →∗ k in Gcp . To prove that no arc can be non-projective, we need to prove that, if cp ∈ C0,m and cp = (σ|i, j|β, Acp ), then Π(p, i, j). (If cp = (σ|i, j|β, Acp ) and Π(p, i, j), then Π(p(cid:2), i, j) for all p(cid:2) such that p < p(cid:2), since in cp every node k such that i < k < j must already have a head.) We prove this by induction over the number ∆(p) of transitions leading to cp from the ﬁrst conﬁguration cp−∆(p) ∈ C0,m such that cp−∆(p) = (σ|i, β, Acp−∆(p) ) (i.e., the ﬁrst conﬁguration where i is on the top of the stack). Basis: If ∆(p) = 0, then i and j are adjacent and Π(p, i, j) holds vacuously. Inductive step: Assume that Π(p, i, j) holds if ∆(p) ≤ q (for some q > 0) and that ∆(P) = q + 1. Now consider the transition tp that
results in conﬁguration cp. There are three cases:

−

l (for some l), then there is a node k

Case 1: If tp = RIGHT-ARCs
such that j < k, (j, l, k) ∈ Acp , and cp−1 = (σ|i|j, k|β, Acp {(j, l, k)}). This entails that there is an earlier conﬁguration cp−r (2 ≤ r ≤ ∆(p)) such that cp−r = (σ|i, j|β, Acp−r ). Because ∆(p − r) = ∆(p) − r ≤ q, we can use the inductive hypothesis to infer Π(p − r, i, j) and hence Π(p, i, j). Case 2: If tp = LEFT-ARCl (for some l), then there is a node k such that i < k < j, (j, l, k) ∈ Acp , and cp−1 = (σ|i|k, j|β, Acp − {(j, l, k)}). Because ∆(p − 1) ≤ q, we can use the inductive hypothesis to infer Π(p − 1, k, j) and, from this, Π(p, k, j). Moreover, because there has to be an earlier conﬁguration cp−r (r < ∆(p)) such that cp−r = (σ|i, k|β, Acp−r ) and ∆(p − r) ≤ q, we can use the inductive hypothesis again to infer Π(p − r, i, k) and Π(p, i, k). Π(p, i, k), Π(p, k, j) and (j, l, k) ∈ Acp together entail Π(p, i, j). Case 3: If the transition tp = SHIFT, then it must have been preceded by a RIGHT-ARCs l transition (for some l), because otherwise i and j would be adjacent. This means that there is a node k such that i < k < j, (i, l, k) ∈ Acp , and cp−2 = (σ|i, k|j|β, Acp − {(i, l, k)}). Because ∆(p − 2) ≤ q, we can again use the inductive hypothesis to infer Π(p − 2, i, k) and Π(p, i, k). Furthermore, it must be the case that either k and j are adjacent or there is an earlier conﬁguration cp−r l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 523 Computational Linguistics Volume 34, Number 4 (r < ∆(p)) such that cp−r = (σ|k, j|β, Acp−r ); in both cases it follows that Π(p, k, j) (in the latter through the inductive hypothesis via Π(p − r, k, j)). As before, Π(p, i, k), Π(p, k, j) and (i, l, k) ∈ Acp together entail Π(p, i, j). For completeness, we need to show that for any sentence x and projective dependency forest Gx = (Vx, Ax) for x, there is a transition sequence C0,m such that Gcm = Gx. We prove this by induction on the length |x| of x = (w0, w1, . . . , wn). Basis: If |x| = 1, then the only projective dependency forest for x is G = ({0}, ∅) and Gcm = Gx for C0,m = (cs(x)). Inductive step: Assume that the claim holds if |x| ≤ p (for some p > 1) E
assume that |X| = p + 1 and Gx = (Vx, Ax) (Vx = {0, 1, . . . , P}). Consider
the subgraph Gx(cid:2) = (Vx − {P}, A−p), where A−p = Ax − {(io, l, j)|i = p ∨ j =
P}, questo è, the graph Gx(cid:2) is exactly like Gx except that the node p and all
the arcs going into or out of this node are missing. It is obvious that,
if Gx is a projective dependency forest for the sentence x = (w0, w1, . . . , wp),
then Gx(cid:2) is a projective dependency forest for the sentence x(cid:2) = (w0, w1, . . . ,
wp−1), and that, because |X(cid:2)| = p, there is a transition sequence C0,q such
that Gcq = Gx(cid:2) (in virtue of the inductive hypothesis). The terminal
conﬁguration of G0,q must have the form cq = (σcq, [ ], A−p), where i ∈ σcq
if and only if i is a root in Gx(cid:2) (else i would have been removed in a
LEFT-ARCl or RIGHT-ARCs
root or a dependent of p. In the latter case, any j such that j ∈ σcq and i < j must also be a dependent of p (else Gx would not be projective, given that i and j are both roots in Gx(cid:2) ). Moreover, if p has a head k in Gx, then k must be the topmost node in σcq that is not a dependent of p (anything else would again be inconsistent with the assumption that Gx is projective). Therefore, we can construct a transition sequence C0,m such that Gcm = Gx, by starting in c0 = cs(x) and applying exactly the same q transitions as in C0,q, followed by as many LEFT-ARCl transitions as there are left dependents of p in Gx, followed by a RIGHT-ARCs l transition if and only if p has a head in Gx, followed by a SHIFT transition (moving the head of p back to the stack and emptying the buffer). (cid:1) l transition). It follows that, in Gx, i is either a Theorem 2 The worst-case time complexity of the arc-standard, stack-based algorithm is O(n), where n is the length of the input sentence. Proof 2 Assuming that the oracle and transition functions can be computed in some constant time, the worst-case running time is bounded by the maximum number of transitions in a transition sequence C0,m for a sentence x = (w0, w1, . . . , wn). Since a SHIFT transition decreases the length of the buffer β by 1, no other transition increases the length of β, and any conﬁguration where β = [ ] is terminal, the number of SHIFT transitions in C0,m is bounded by n. Moreover, since both LEFT-ARCl and RIGHT-ARCs l decrease the height of the stack by 1, only SHIFT increases the height of the stack by 1, and the initial height of the stack is 1, the combined number of instances of LEFT-ARCl and RIGHT-ARCs l in C0,m is also bounded by n. Hence, the worst case time complexity is O(n). (cid:1) 524 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Nivre Deterministic Incremental Dependency Parsing Remark 1 The assumption that the oracle function can be computed in constant time will be dis- cussed at the end of Section 6.1, where we approximate oracles with treebank-induced classiﬁers in order to experimentally evaluate the different algorithms. The assumption that every transition can be performed in constant time can be justiﬁed by noting that the only operations involved are those of adding an arc to the graph, removing the ﬁrst element from the buffer, and pushing or popping the stack. Theorem 3 The worst-case space complexity of the arc-standard, stack-based algorithm is O(n), where n is the length of the input sentence. Proof 3 Given the deterministic parsing algorithm, only one conﬁguration c = (σ, β, A) needs to be stored at any given time. Assuming that a single node can be stored in some constant space, the space needed to store σ and β, respectively, is bounded by the number of nodes. The same holds for A, given that a single arc can be stored in constant space, because the number of arcs in a dependency forest is bounded by the number of nodes. Hence, the worst-case space complexity is O(n). (cid:1) 4.2 Arc-Eager Parsing The transition set T for the arc-eager, stack-based parser is deﬁned in Figure 5 and contains four types of transitions: 1. Transitions LEFT-ARCl (for any dependency label l) add a dependency arc (j, l, i) to A, where i is the node on top of the stack σ and j is the ﬁrst node in the buffer β. In addition, they pop the stack σ. They have as a precondition that the token i is not the artiﬁcial root node 0 and does not already have a head. Transitions LEFT-ARCl RIGHT-ARCe l REDUCE SHIFT Preconditions LEFT-ARCl RIGHT-ARCe l REDUCE (σ|i, j|β, A) ⇒ (σ, j|β, A∪{(j, l, i)}) (σ|i, j|β, A) ⇒ (σ|i|j, β, A∪{(i, l, j)}) (σ|i, β, A) ⇒ (σ, β, A) (σ, i|β, A) ⇒ (σ|i, β, A) ¬[i = 0] ¬∃k∃l(cid:2)[(k, l(cid:2), i) ∈ A] ¬∃k∃l(cid:2)[(k, l(cid:2), j) ∈ A] ∃k∃l[(k, l, i) ∈ A] Figure 5 Transitions for the arc-eager, stack-based parsing algorithm. 525 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Computational Linguistics Volume 34, Number 4 2. 3. 4. Transitions RIGHT-ARCe l (for any dependency label l) add a dependency arc (i, l, j) to A, where i is the node on top of the stack σ and j is the ﬁrst node in the buffer β. In addition, they remove the ﬁrst node j in the buffer β and push it on top of the stack σ. They have as a precondition that the token j does not already have a head. The transition REDUCE pops the stack β and is subject to the precondition that the top token has a head. The transition SHIFT removes the ﬁrst node i in the buffer β and pushes it on top of the stack σ. The arc-eager parser differs from the arc-standard one by attaching right dependents (using RIGHT-ARCe l transitions) as soon as possible, that is, before the right dependent has found all its right dependents. As a consequence, the RIGHT-ARCe l transitions cannot replace the head-dependent structure with the head, as in the arc-standard system, but must store both the head and the dependent on the stack for further processing. The dependent can be popped from the stack at a later time through the REDUCE transition, which completes the reduction of this structure. The arc-eager system is illustrated in Figure 6, which shows the transition sequence needed to parse the English sentence in Figure 2 with the same output as the arc-standard sequence in Figure 4. Theorem 4 The arc-eager, stack-based algorithm is correct for the class of projective dependency forests. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o RIGHT-ARCe Transition Conﬁguration ( SHIFT =⇒ ( LEFT-ARCNMOD =⇒ ( SHIFT =⇒ ( LEFT-ARCSBJ =⇒ ( ROOT =⇒ ( SHIFT =⇒ ( LEFT-ARCNMOD =⇒ ( OBJ =⇒ ( RIGHT-ARCe NMOD =⇒ ( SHIFT =⇒ ( LEFT-ARCNMOD =⇒ ( PMOD =⇒ ( RIGHT-ARCe REDUCE =⇒ ( REDUCE =⇒ ( REDUCE =⇒ ( P =⇒ ( [0], [0, 1], [0], [0, 2], [0], [0, 3], [0, 3, 4], [0, 3], [0, 3, 5], [0, . . . , 6], [0, . . . , 7], [0, . . . 6], [0, . . . , 8], [0, . . . , 6], [0, 3, 5], [0, 3], [0, 3, 9], RIGHT-ARCe RIGHT-ARCe ∅ ∅ [1, . . . , 9], [2, . . . , 9], [2, . . . , 9], A1 = {(2, NMOD, 1)} [3, . . . , 9], A1 [3, . . . , 9], A2 = A1 ∪{(3, SBJ, 2)} [4, . . . , 9], A3 = A2 ∪{(0, ROOT, 3)} [5, . . . , 9], A3 [5, . . . , 9], A4 = A3 ∪{(5, NMOD, 4)} [6, . . . , 9], A5 = A4 ∪{(3, OBJ, 5)} [7, 8, 9], [8, 9], [8, 9], [9], [9], [9], [9], [ ], A6 = A5 ∪{(5, NMOD, 6)} A6 A7 = A6 ∪{(8, NMOD, 7)} A8 = A7 ∪{(6, PMOD, 8)} A8 A8 A8 A9 = A8 ∪{(3, P, 9)} l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) Figure 6 Arc-eager transition sequence for the English sentence in Figure 2. 526 Nivre Deterministic Incremental Dependency Parsing Proof 4 To show the soundness of the algorithm, we show that the dependency graph deﬁned by the initial conﬁguration, Gc0(x) = (Vx, ∅), is a projective dependency forest, and that every transition preserves this property. We consider each of the relevant conditions in turn, keeping in mind that the only transitions that modify the graph are LEFT-ARCl and RIGHT-ARCe l . 1. 2. 3. 4. ROOT: Same as Proof 1. SINGLE-HEAD: Same as Proof 1 (substitute RIGHT-ARCe l for RIGHT-ARCs l ). ACYCLICITY: Gcs(x) is acyclic, and adding an arc (i, l, j) will create a cycle only if there is a directed path from j to i. In the case of LEFT-ARCl, this would require the existence of a conﬁguration cp = (σ|j, i|β, Acp ) such that (k, l(cid:2), i) ∈ Acp (for some k and l(cid:2)), which is impossible because any transition adding an arc (k, l(cid:2), i) has as a consequence that i is no longer in the buffer. In the case of RIGHT-ARCe l , this would require a conﬁguration cp = (σ|i, j|β, Acp ) such that, given the arcs in Acp , there is a directed path from j to i. Such a path would have to involve at least one arc (k, l(cid:2), k(cid:2)) such that k(cid:2) ≤ i < k, which would entail that i is no longer in σ. (If k(cid:2) = i, then i would be popped in the LEFT-ARCl(cid:2) transition adding the arc; if k(cid:2) < i, then i would have to be popped before the arc could be added.) PROJECTIVITY: To prove that, if cp ∈ C0,m and cp = (σ|i, j|β, Acp ), then Π(p, i, j), we use essentially the same technique as in Proof 1, only with different cases in the inductive step because of the different transitions. As before, we let ∆(p) be the number of transitions that it takes to reach cp from the ﬁrst conﬁguration that has i on top of the stack. Basis: If ∆(p) = 0, then i and j are adjacent, which entails Π(p, i, j). Inductive step: We assume that Π(p, i, j) holds if ∆(p) ≤ q (for some q > 0) and that ∆(P) = q + 1, and we concentrate on the transition
tp that results in conﬁguration cp. For the arc-eager algorithm, there
are only two cases to consider, because if tp = RIGHT-ARCe
some l) or tp = SHIFT then ∆(P) = 0, which contradicts our
assumption that ∆(P) > q > 0. (This follows because the arc-eager
algorithm, unlike its arc-standard counterpart, does not allow
nodes to be moved back from the stack to the buffer.)

l (for

Case 1: If tp = LEFT-ARCl (for some l), then there is a node k
such that i < k < j, (j, l, k) ∈ Acp , and cp−1 = (σ|i|k, j|β, Acp − {(j, l, k)}). Because ∆(p − 1) ≤ q, we can use the inductive hypothesis to infer Π(p − 1, k, j) and, from this, Π(p, k, j). Moreover, because there has to be an earlier conﬁguration cp−r (r < ∆(p)) such that cp−r = (σ|i, k|β, Acp−r ) and ∆(p − r) ≤ q, we can use the inductive hypothesis again to infer Π(p − r, i, k) and Π(p, i, k). Π(p, i, k), Π(p, k, j) and (j, l, k) ∈ Acp together entail Π(p, i, j). Case 2: If the transition tp = REDUCE, then there is a node k such that i < k < j, (i, l, k) ∈ Acp , and cp−1 = (σ|i|k, j|β, Acp ). Because ∆(p − 1) ≤ q, we can again use the inductive hypothesis to infer Π(p − 1, k, j) and Π(p, k, j). Moreover, l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 527 Computational Linguistics Volume 34, Number 4 there must be an earlier conﬁguration cp−r (r < ∆(p)) such that cp−r = (σ|i, k|β, Acp−r ) and ∆(p − r) ≤ q, which entails Π(p − r, i, k) and Π(p, i, k). As before, Π(p, i, k), Π(p, k, j) and (i, l, k) ∈ Acp together entail Π(p, i, j). For completeness, we need to show that for any sentence x and projective depen- dency forest Gx = (Vx, Ax) for x, there is a transition sequence C0,m such that Gcm = Gx. Using the same idea as in Proof 1, we prove this by induction on the length |x| of x = (w0, w1, . . . , wn). Basis: If |x| = 1, then the only projective dependency forest for x is G = ({0}, ∅) and Gcm = Gx for C0,m = (cs(x)). Inductive step: Assume that the claim holds if |x| ≤ p (for some p > 1)
and assume that |X| = p + 1 and Gx = (Vx, Ax) (Vx = {0, 1, . . . , P}). As in
Proof 1, we may now assume that there exists a transition sequence C0,q
for the sentence x(cid:2) = (w0, w1, wp−1) and subgraph Gx(cid:2) = (Vx − {P}, A−p),
where the terminal conﬁguration has the form cq = (σcq, [ ], A−p). For the
arc-eager algorithm, if i is a root in Gx(cid:2) then i ∈ σcq; but if i ∈ σcq then i is
either a root or has a head j such that j < i in Gx(cid:2) . (This is because i may have been pushed onto the stack in a RIGHT-ARCe l transition and may or may not have been popped in a later REDUCE transition.) Apart from the possibility of unreduced right dependents, we can use the same reasoning as in Proof 1 to show that, for any i ∈ σcq that is a root in Gx(cid:2) , if i is a dependent of p in Gx then any j such that j ∈ σcq, i < j and j is a root in Gx(cid:2) must also be a dependent of p in Gx (or else Gx would fail to be projective). Moreover, if p has a head k in Gx, then k must be in σcq and any j such that j ∈ σcq and k < j must either be a dependent of p in Gx or must have a head to the left in both Gx(cid:2) and Gx (anything else would again be inconsistent with the assumption that Gx is projective). Therefore, we can construct a transition sequence C0,m such that Gcm = Gx, by starting in c0 = cs(x) and applying exactly the same q transitions as in C0,q, followed by as many LEFT-ARCl transitions as there are left dependents of p in Gx, interleaving REDUCE transitions whenever the node on top of the stack already has a head, followed by a RIGHT-ARCe l transition if p has a head in Gx and a SHIFT transition otherwise (in both cases moving p to the stack and emptying the buffer). (cid:1) Theorem 5 The worst-case time complexity of the arc-eager, stack-based algorithm is O(n), where n is the length of the input sentence. Proof 5 The proof is essentially the same as Proof 2, except that both SHIFT and RIGHT-ARCe l decrease the length of β and increase the height of σ, while both REDUCE and LEFT- ARCl decrease the height of σ. Hence, the combined number of SHIFT and RIGHT-ARCe l transitions, as well as the combined number of REDUCE and LEFT-ARCl transitions, are bounded by n. (cid:1) 528 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Nivre Deterministic Incremental Dependency Parsing Theorem 6 The worst-case space complexity of the arc-eager, stack-based algorithm is O(n), where n is the length of the input sentence. Proof 6 Same as Proof 3. (cid:1) 5. List-Based Algorithms The list-based algorithms make use of two lists to store partially processed tokens, that is, tokens that have been removed from the input buffer but which are still considered as potential candidates for dependency links, either as heads or as dependents. A parser conﬁguration is therefore deﬁned as a quadruple, consisting of two lists, an input buffer, and a set of dependency arcs. Deﬁnition 11 A list-based conﬁguration for a sentence x = (w0, w1, . . . , wn) is a quadruple c = (λ1, λ2, β, A), where 1. 2. 3. 4. λ1 is a list of tokens i1 ≤ k1 (for some k1 ≤ n), λ2 is a list of tokens i2 ≤ k2 (for some k2, k1 < k2 ≤ n), β is a buffer of tokens j > k,
A is a set of dependency arcs such that G = ({0, 1, . . . , N}, UN) is a
dependency graph for x.

The list λ1 has its head to the right and stores nodes in descending order, and the list λ2
has its head to the left and stores nodes in ascending order. Così, λ1|i represents a list
with head i and tail λ1, whereas j|λ2 represents a list with head j and tail λ2.4 We use
square brackets for enumerated lists as before, and we write λ1.λ2 for the concatenation
of λ1 and λ2, so that, Per esempio, [0, 1].[2, 3, 4] = [0, 1, 2, 3, 4]. The notational conven-
tions for the buffer β and the set A of dependency arcs are the same as before.

Deﬁnition 12
A list-based transition system is a quadruple S = (C, T, cs, Ct), Dove

C is the set of all list-based conﬁgurations,
cs(x = (w0, w1, . . . wn)) = ([0], [ ], [1, . . . , N], ∅),
T is a set of transitions, each of which is a function t : C → C,
Ct = {c ∈ C|c = (λ1, λ2, [ ], UN)}.

A list-based parse of a sentence x = (w0, w1, . . . , wn) starts with the artiﬁcial root node 0
as the sole element of λ1, an empty list λ2, all the nodes corresponding to real words in
the buffer β, and an empty set A of dependency arcs; it ends as soon as the buffer β is
empty. Così, the only difference compared to the stack-based systems is that we have
two lists instead of a single stack. Otherwise, both initialization and termination are

4 The operator | is taken to be left-associative for λ1 and right-associative for λ2.

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Linguistica computazionale

Volume 34, Numero 4

Transitions

LEFT-ARCn
l

RIGHT-ARCn
l

NO-ARCn

SHIFT

Preconditions

LEFT-ARCn
l

RIGHT-ARCn
l

(λ1|io, λ2, j|β, UN) ⇒ (λ1, io|λ2, j|β, A∪{(j, l, io)})
(λ1|io, λ2, j|β, UN) ⇒ (λ1, io|λ2, j|β, A∪{(io, l, j)})
(λ1|io, λ2, β, UN) ⇒ (λ1, io|λ2, β, UN)
(λ1, λ2, io|β, UN) ⇒ (λ1.λ2|io, [ ], β, UN)

¬[i = 0]
¬∃k∃l(cid:2)[(k, l(cid:2), io) ∈ A]
¬[i →∗ j]UN
¬∃k∃l(cid:2)[(k, l(cid:2), j) ∈ A]
¬[j →∗ i]UN

Figura 7
Transitions for the non-projective, list-based parsing algorithm.

essentially the same. The transitions used by list-based parsers are again composed of
two types of actions: adding (labeled) arcs to A and manipulating the lists λ1 and λ2, E
the input buffer β. By combining such actions in different ways, we can construct transi-
tion systems with different properties. We will now deﬁne two such systems, which we
call non-projective and projective, rispettivamente, after the classes of dependency graphs
that they can handle.

A clariﬁcation may be in order concerning the use of lists instead of stacks for this
family of algorithms. Infatti, most of the transitions to be deﬁned subsequently make
no essential use of this added ﬂexibility and could equally well have been formalized
using two stacks instead. Tuttavia, we will sometimes need to append two lists into
one, and this would not be a constant-time operation using standard stack operations.
We therefore prefer to deﬁne these structures as lists, even though they will mostly be
used as stacks.

5.1 Non-Projective Parsing

The transition set T for the non-projective, list-based parser is deﬁned in Figure 7 E
contains four types of transitions:

l (for any dependency label l) add a dependency arc

Transitions LEFT-ARCn
(j, l, io) to A, where i is the head of the list λ1 and j is the ﬁrst node in the
buffer β. Inoltre, they move i from the list λ1 to the list λ2. They have
as a precondition that the token i is not the artiﬁcial root node and does
not already have a head. Inoltre, there must not be a path from i to j in
the graph G = ({0, 1, . . . , N}, UN).5

5 We use the notation [i −→∗

j]A to signify that there is a path connecting i and j in G = ({0, 1, . . . , N}, UN).

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Nivre

Deterministic Incremental Dependency Parsing

l (for any dependency label l) add a dependency

Transitions RIGHT-ARCn
arc (io, l, j) to A, where i is the head of the list λ1 and j is the ﬁrst node in the
buffer β. Inoltre, they move i from the list λ1 to the list λ2. They have
as a precondition that the token j does not already have a head and that
there is no path from j to i in G = ({0, 1, . . . , N}, UN).

The transition NO-ARC removes the head i of the list λ1 and inserts it at
the head of the list λ2.
The transition SHIFT removes the ﬁrst node i in the buffer β and inserts it
at the head of a list obtained by concatenating λ1 and λ2. This list becomes
the new λ1, whereas λ2 is empty in the resulting conﬁguration.

The non-projective, list-based parser essentially builds a dependency graph by consid-
ering every pair of nodes (io, j) (io < j) and deciding whether to add a dependency arc between them (in either direction), although the SHIFT transition allows it to skip certain pairs. More precisely, if i is the head of λ1 and j is the ﬁrst node in the buffer β when a SHIFT transition is performed, then all pairs (k, j) such that k < i are ignored. The fact that both the head and the dependent are kept in either λ2 or β makes it possible to construct non-projective dependency graphs, because the NO-ARCn transition allows a node to be passed from λ1 to λ2 even if it does not (yet) have a head. However, an arc can only be added between two nodes i and j if the dependent end of the arc is not the artiﬁcial root 0 and does not already have a head, which would violate ROOT and SINGLE-HEAD, respectively, and if there is no path connecting the dependent to the head, which would cause a violation of ACYCLICITY. As an illustration, Figure 8 shows the transition sequence needed to parse the Czech sentence in Figure 1, which has a non-projective dependency graph. Theorem 7 The non-projective, list-based algorithm is correct for the class of dependency forests. Proof 7 To show the soundness of the algorithm, we simply observe that the dependency graph deﬁned by the initial conﬁguration, Gc0(x) = ({0, 1, . . . , n}, ∅), satisﬁes ROOT, SINGLE- HEAD, and ACYCLICITY, and that none of the four transitions may lead to a violation λ and NO-ARCn do not modify the graph at of these constraints. (The transitions SHIFT all, and LEFT-ARCn l have explicit preconditions to prevent this.) l and RIGHT-ARCn For completeness, we need to show that for any sentence x and dependency for- est Gx = (Vx, Ax) for x, there is a transition sequence C0,m such that Gcm = Gx. Using the same idea as in Proof 1, we prove this by induction on the length |x| of x = (w0, w1, . . . , wn). Basis: If |x| = 1, then the only dependency forest for x is G = ({0}, ∅) and Gcm = Gx for C0,m = (cs(x)). Inductive step: Assume that the claim holds if |x| ≤ p (for some p > 1)
and assume that |X| = p + 1 and Gx = (Vx, Ax) (Vx = {0, 1, . . . , P}). As in
Proof 1, we may now assume that there exists a transition sequence C0,q
for the sentence x(cid:2) = (w0, w1, wp−1) and subgraph Gx(cid:2) = (Vx − {P}, A−p),
but the terminal conﬁguration now has the form cq = (λcq , [ ], [ ], A−p),
where λcq = [0, 1, . . . , p − 1]. In order to construct a transition sequence
C0,m such that Gcm = Gx we instead start from the conﬁguration

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Linguistica computazionale

Volume 34, Numero 4

SHIFT
RIGHT-ARCn
SHIFT

( [0],

λ =⇒ ( [0, 1],
Atr =⇒ ( [0],
λ =⇒ ( [0, 1, 2],

Transition Conﬁguration
[ ],
[ ],
[1],
[ ],
[2],
[1, 2],
[0, 1, 2],

NO-ARCn =⇒ ( [0, 1],
NO-ARCn =⇒ ( [0],
Pred =⇒ ( [ ],

RIGHT-ARCn
SHIFT
SHIFT

LEFT-ARCn
RIGHT-ARCn

λ =⇒ ( [0, . . . , 3], [ ],
λ =⇒ ( [0, . . . , 4], [ ],
AuxZ =⇒ ( [0, . . . , 3], [4],

Sb =⇒ ( [0, 1, 2],

NO-ARCn =⇒ ( [0, 1],

LEFT-ARCn

AuxP =⇒ ( [0],

SHIFT

λ =⇒ ( [0, . . . , 5], [ ],
NO-ARCn =⇒ ( [0, . . . , 4], [5],
NO-ARCn =⇒ ( [0, . . . , 3], [4, 5],
AuxP =⇒ ( [0, 1, 2],

RIGHT-ARCn

SHIFT
RIGHT-ARCn
SHIFT

λ =⇒ ( [0, . . . , 6], [ ],
Adv =⇒ ( [0, . . . , 5], [6],
λ =⇒ ( [0, . . . , 7], [ ],
[8],
NO-ARCn =⇒ ( [0, . . . , 6], [7],
[8],
NO-ARCn =⇒ ( [0, . . . , 5], [6, 7],
[8],
NO-ARCn =⇒ ( [0, . . . , 4], [5, 6, 7],
[8],
NO-ARCn =⇒ ( [0, . . . , 3], [4, . . . , 7], [8],
NO-ARCn =⇒ ( [0, 1, 2],
[3, . . . , 7], [8],
NO-ARCn =⇒ ( [0, 1],
[2, . . . , 7], [8],
NO-ARCn =⇒ ( [0],
[1, . . . , 7], [8],
AuxK =⇒ ( [ ],
[0, . . . , 7], [8],
[ ],

λ =⇒ ( [0, . . . , 8], [ ],

SHIFT

RIGHT-ARCn

[1, . . . , 8], ∅
)
[2, . . . , 8], ∅
)
[2, . . . , 8], A1 = {(1, Atr, 2)}
)
[3, . . . , 8], A1
)
[3, . . . , 8], A1
)
[3, . . . , 8], A1
)
[3, . . . , 8], A2 = A1 ∪{(0, Pred, 3)}
)
[4, . . . , 8], A2
)
[5, . . . , 8], A2
)
[5, . . . , 8], A3 = A2 ∪{(5, AuxZ, 4)} )
[5, . . . , 8], A4 = A3 ∪{(3, Sb, 5)}
)
[3, 4],
[5, . . . , 8], A4
[2, 3, 4],
)
[1, . . . , 4], [5, . . . , 8], A5 = A4 ∪{(5, AuxP, 1)} )
A5
)
A5
)
A5
)
A6 = A5 ∪{(3, AuxP, 6)} )
A6
)
A7 = A6 ∪{(6, Adv, 7)}
)
A7
)
A7
)
A7
)
A7
)
A7
)
A7
)
A7
)
A7
)
A8 = A7 ∪{(0, AuxK, 8)} )
A8
)

[6, 7, 8],
[6, 7, 8],
[6, 7, 8],
[6, 7, 8],
[7, 8],
[7, 8],

[3, 4, 5],

Figura 8
Non-projective transition sequence for the Czech sentence in Figure 1.

c0 = cs(X) and apply exactly the same q transitions, reaching the
conﬁguration cq = (λcq , [ ], [P], A−p). We then perform exactly p transitions,
in each case choosing LEFT-ARCn
dependent of p in Gx (with label l), RIGHT-ARCn
label l(cid:2)) and NO-ARCn otherwise. One ﬁnal SHIFT
|P, [ ], [ ], Ax). (cid:1)
the terminal conﬁguration cm = (λcq

l(cid:2) if i is the head of p (con
λ transition takes us to

l if the token i at the head of λ1 is a

Theorem 8
The worst-case time complexity of the non-projective, list-based algorithm is O(n2),
where n is the length of the input sentence.

Proof 8
Assuming that the oracle and transition functions can be performed in some constant
time, the worst-case running time is bounded by the maximum number of transitions

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Nivre

Deterministic Incremental Dependency Parsing

in a transition sequence C0,m for a sentence x = (w0, w1, . . . , wn). As for the stack-based
λ transitions in C0,m. Inoltre, because each of
algorithms, there can be at most n SHIFT
the three other transitions presupposes that λ1 is non-empty and decreases its length by
1, there can be at most i such transitions between the i−1th and the ith SHIFT transition.
N
i=1 i+1, che è
It follows that the total number of transitions in C0,m is bounded by
O(n2). (cid:1)

(cid:1)

Remark 2
The assumption that transitions can be performed in constant time can be justiﬁed by
the same kind of considerations as for the stack-based algorithms (cf. Remark 1). IL
λ transition, which involves appending the two lists λ1
only complication is the SHIFT
and λ2, but this can be handled with an appropriate choice of data structures. A more
serious complication is the need to check the preconditions of LEFT-ARCn
l and RIGHT-
ARCn
l , but if we assume that it is the responsibility of the oracle to ensure that the
preconditions of any predicted transition are satisﬁed, we can postpone the discussion
of this problem until the end of Section 6.1.

Theorem 9
The worst-case space complexity of the non-projective, list-based algorithm is O(N),
where n is the length of the input sentence.

Proof 9
Given the deterministic parsing algorithm, only one conﬁguration c = (λ1, λ2, β, UN)
needs to be stored at any given time. Assuming that a single node can be stored in
some constant space, the space needed to store λ1, λ2, and β, rispettivamente, is bounded
by the number of nodes. The same holds for A, given that a single arc can be stored in
constant space, because the number of arcs in a dependency forest is bounded by the
number of nodes. Hence, the worst-case space complexity is O(N). (cid:1)

5.2 Projective Parsing

The transition set T for the projective, list-based parser is deﬁned in Figure 9 E
contains four types of transitions:

P
l (for any dependency label l) add a dependency arc

Transitions LEFT-ARC
(j, l, io) to A, where i is the head of the list λ1 and j is the ﬁrst node in the
buffer β. Inoltre, they remove i from the list λ1 and empty λ2. They
have as a precondition that the token i is not the artiﬁcial root node and
does not already have a head.

P
l (for any dependency label l) add a dependency

Transitions RIGHT-ARC
arc (io, l, j) to A, where i is the head of the list λ1 and j is the ﬁrst node in the
buffer β. Inoltre, they move j from the buffer β and empty the list λ2.
They have as a precondition that the token j does not already have a head.
The transition NO-ARCp removes the head i of the list λ1 and inserts it at
the head of the list λ2. It has as a precondition that the node i already has
a head.

λ removes the ﬁrst node i in the buffer β and inserts it
The transition SHIFT
at the head of a list obtained by concatenating λ1 and λ2. This list becomes
the new λ1, while λ2 is empty in the resulting conﬁguration.

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Linguistica computazionale

Volume 34, Numero 4

Transitions
P
l

LEFT-ARC

RIGHT-ARC

P
l

NO-ARCp

SHIFT

Preconditions

LEFT-ARC

P
l

RIGHT-ARC

P
l

NO-ARCp

(λ1|io, λ2, j|β, UN) ⇒ (λ1, [ ], j|β, A∪{(j, l, io)})
(λ1|io, λ2, j|β, UN) ⇒ (λ1|io|j, [ ], β, A∪{(io, l, j)})
(λ1|io, λ2, β, UN) ⇒ (λ1, io|λ2, β, UN)
(λ1, λ2, io|β, UN) ⇒ (λ1.λ2|io, [ ], β, UN)

¬[i = 0]
¬∃k∃l(cid:2)[(k, l(cid:2), io) ∈ A]
¬∃k∃l(cid:2)[(k, l(cid:2), j) ∈ A]
∃k∃l[(k, l, io) ∈ A]

Figura 9
Transitions for the projective, list-based parsing algorithm.

The projective, list-based parser uses the same basic strategy as its non-projective coun-
terpart, but skips any pair (io, j) that could give rise to a non-projective dependency arc.
The essential differences are the following:

1. While LEFT-ARCn

l stores the dependent i in the list λ2, allowing it to have
P
dependents to the right of j, LEFT-ARC
l deletes it and in addition empties
λ2 because any dependency arc linking i, or any node between i and j, ad a
node succeeding j would violate PROJECTIVITY.

2. While RIGHT-ARCn

l allows the dependent j to seek dependents to the

λ transition by moving j to λ1|io, because any

left of i, by simply moving i from λ1 to λ2, RIGHT-ARC
incorporates a SHIFT
dependency arc linking j to a node preceding i would violate
PROJECTIVITY. Inoltre, it does not move any nodes from λ2 to λ1,
since these nodes can no longer be linked to any node succeeding j
without violating PROJECTIVITY.

P
l essentially

3. While NO-ARCn is permissible as long as λ1 is not empty, NO-ARCp

requires that the node i already has a head because any dependency arc
spanning a root node would violate PROJECTIVITY (regardless of which
arcs are added later).

The fact that the projective algorithm skips many node pairs that are considered by the
non-projective algorithm makes it more efﬁcient in practice, although the worst-case
time complexity remains the same. Figura 10 shows the transition sequence needed
to parse the English sentence in Figure 2 with the same output as the stack-based
sequences in Figures 4 E 6.

Theorem 10
The projective, list-based algorithm is correct for the class of projective dependency
forests.

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Nivre

Deterministic Incremental Dependency Parsing

LEFT-ARC

RIGHT-ARC

( [0],

λ =⇒ ( [0, 1],

λ =⇒ ( [0, 3, 4],

SHIFT
P
NMOD =⇒ ( [0],

λ =⇒ ( [0, 2],
SHIFT
P
SBJ =⇒ ( [0],
LEFT-ARC
P
ROOT =⇒ ( [0, 3],

SHIFT
P
NMOD =⇒ ( [0, 3],

Transition Conﬁguration
[ ],
[ ],
[ ],
[ ],
[ ],
[ ],
[ ],
[ ],
[ ],
P
NMOD =⇒ ( [0, . . . , 6], [ ],
λ =⇒ ( [0, . . . , 7], [ ],
[ ],
P
PMOD =⇒ ( [0, . . . , 8], [ ],
NO-ARCp =⇒ ( [0, . . . , 6], [8],
NO-ARCp =⇒ ( [0, 3, 5],
NO-ARCp =⇒ ( [0, 3],

P
OBJ =⇒ ( [0, 3, 5],

SHIFT

LEFT-ARCNMOD =⇒ ( [0, . . . 6],
RIGHT-ARC

LEFT-ARC
RIGHT-ARC

RIGHT-ARC

P
P =⇒ ( [0, 3, 9],

[1, . . . , 9], ∅
)
[2, . . . , 9], ∅
)
[2, . . . , 9], A1 = {(2, NMOD, 1)}
)
[3, . . . , 9], A1
)
[3, . . . , 9], A2 = A1 ∪{(3, SBJ, 2)}
)
[4, . . . , 9], A3 = A2 ∪{(0, ROOT, 3)} )
[5, . . . , 9], A3
)
[5, . . . , 9], A4 = A3 ∪{(5, NMOD, 4)} )
[6, . . . , 9], A5 = A4 ∪{(3, OBJ, 5)}
)
A6 = A5 ∪{(5, NMOD, 6)} )
[7, 8, 9],
A6
[8, 9],
)
A7 = A6 ∪{(8, NMOD, 7)} )
[8, 9],
A8 = A7 ∪{(6, PMOD, 8)} )
[9],
A8
)
[9],
A8
)
[9],
[6, 8],
A8
)
[5, 6, 8], [9],
A9 = A8 ∪{(3, P, 9)}
)
[ ],
[ ],

Figura 10
Projective transition sequence for the English sentence in Figure 2.

Proof 10
To show the soundness of the algorithm, we show that the dependency graph deﬁned
by the initial conﬁguration, Gc0(X) = (V, ∅), is a projective dependency forest, and that
every transition preserves this property. We consider each of the relevant conditions in
P
turn, keeping in mind that the only transitions that modify the graph are LEFT-ARC
l
and RIGHT-ARC

P
l .

ROOT: Same as Proof 1 (substitute LEFT-ARC

SINGLE-HEAD: Same as Proof 1 (substitute LEFT-ARC
for LEFT-ARCl and RIGHT-ARCs

l , rispettivamente).

P
l for LEFT-ARCl).
P
l and RIGHT-ARC

P
l

ACYCLICITY: Gcs(X) is acyclic, and adding an arc (io, l, j) will create a cycle
P
only if there is a directed path from j to i. In the case of LEFT-ARC
l , Questo
would require the existence of a conﬁguration cp = (λ1|j, λ2, io|β, Acp ) come
Quello (k, l(cid:2), io) ∈ Acp (for some k < i and l(cid:2)), which is impossible because any transition adding an arc (k, l(cid:2), i) has as a consequence that i is no longer in p the buffer. In the case of RIGHT-ARC l , this would require a conﬁguration cp = (λ1|i, λ2, j|β, Acp ) such that, given the arcs in Acp , there is a directed path from j to i. Such a path would have to involve at least one arc (k, l(cid:2), k(cid:2)) such that k(cid:2) ≤ i < k, which would entail that i is no longer in λ1 or λ2. (If k(cid:2) = i, then i would be removed from λ1—and not added to λ2—in the LEFT-ARC moved to λ2 before the arc can be added and removed as this list is emptied in the LEFT-ARC l(cid:2) transition adding the arc; if k(cid:2) < i, then i would have to be p p l(cid:2) transition.) 535 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Computational Linguistics Volume 34, Number 4 4. PROJECTIVITY: Gcs(x) is projective, and adding an arc (i, l, j) will make the graph non-projective only if there is a node k such that i < k < j or j < k < i and neither i →∗ k nor j →∗ k. Let C0,m be a conﬁguration sequence for x = (w0, w1, . . . , wn) and let Π(p, i, j) (for 0 < p < m, 0 ≤ i < j ≤ n) be the claim that, for every k such that i < k < j, i →∗ k or j →∗ k in Gcp . To prove that no arc can be non-projective, we need to prove that, if cp ∈ C0,m and cp = (λ1|i, λ2, j|β, Acp ), then Π(p, i, j). (If cp = (λ1|i, λ2, j|β, Acp ) and Π(p, i, j), then Π(p(cid:2), i, j) for all p(cid:2) such that p < p(cid:2), because in cp every node k such that i < k < j must already have a head.) We prove this by induction over the number ∆(p) of transitions leading to cp from the ﬁrst conﬁguration cp−∆(p) ∈ C0,m such that cp−∆(p) = (λ1, λ2, j|β, Acp−∆(p) ) (i.e., the ﬁrst conﬁguration where j is the ﬁrst node in the buffer). Basis: If ∆(p) = 0, then i and j are adjacent and Π(p, i, j) holds vacuously. Inductive step: Assume that Π(p, i, j) holds if ∆(p) ≤ q (for some q > 0) and that ∆(P) = q + 1. Now consider the transition tp that
results in conﬁguration cp. For the projective, list-based algorithm,
there are only two cases to consider, because if tp = RIGHT-ARC
(for some l) or tp = SHIFT then ∆(P) = 0, which contradicts our
assumption that ∆(P) > q > 0. (This follows because there is no
transition that moves a node back to the buffer.)

P
l

P
l (for some l), then there is a node k

Case 1: If tp = LEFT-ARC
−
such that i < k < j, (j, l, k) ∈ Acp, cp−1 = (λ1|i|k, λ2, j|β, Acp {(j, l, k)}), and cp = (λ1|i, [ ], j|β, Acp ). Because ∆(p − 1) ≤ q, we can use the inductive hypothesis to infer Π(p − 1, k, j) and, from this, Π(p, k, j). Moreover, because there has to be an earlier conﬁguration cp−r (r < ∆(p)) such that cp−r = (λ1|i, λ2(cid:2) , k|β, Acp−r ) and ∆(p − r) ≤ q, we can use the inductive hypothesis again to infer Π(p − r, i, k) and Π(p, i, k). Π(p, i, k), Π(p, k, j), and (j, l, k) ∈ Acp together entail Π(p, i, j). Case 2: If the transition tp = NO-ARCp, then there is a node k such that i < k < j, (i, l, k) ∈ Acp , cp−1 = (λ1|i|k, λ2, j|β, Acp ), and cp = (λ1|i, k|λ2, j|β, Acp ). Because ∆(p − 1) ≤ q, we can again use the inductive hypothesis to infer Π(p − 1, k, j) and Π(p, k, j). Moreover, there must be an earlier conﬁguration cp−r (r < ∆(p)) such that cp−r = (λ1|i, λ2(cid:2) , k|β, Acp−r ) and ∆(p − r) ≤ q, which entails Π(p − r, i, k) and Π(p, i, k). As before, Π(p, i, k), Π(p, k, j), and (i, l, k) ∈ Acp together entail Π(p, i, j). For completeness, we need to show that for any sentence x and dependency forest Gx = (Vx, Ax) for x, there is a transition sequence C0,m such that Gcm = Gx. The proof is by induction on the length |x| and is essentially the same as Proof 7 up to the point where we assume the existence of a transition sequence C0,q for the sentence x(cid:2) = (w0, w1, wp−1) and subgraph Gx(cid:2) = (Vx − {p}, A−p), where the terminal conﬁguration still has the form cq = (λcq , [ ], [ ], A−p), but where it can no longer be assumed that λcq = [0, 1, . . . , p − 1]. If i is a root in Gx(cid:2) then i ∈ λcq ; but if i ∈ λcq then i is either a root or has a head j such 536 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Nivre Deterministic Incremental Dependency Parsing p p l transition leaves the dependent in λ1 that j < i in Gx(cid:2) . (This is because a RIGHT-ARC l removes it.) Moreover, for any i ∈ λcq that is a root in Gx(cid:2) , if i is a while a LEFT-ARC dependent of p in Gx then any j such that j ∈ λcq , i < j and j is a root in Gx(cid:2) must also be a dependent of p in Gx (else Gx would fail to be projective). Finally, if p has a head k in Gx, then k must be in λcq and any j such that j ∈ λcq and k < j must either be a dependent of p in Gx or must have a head to the left in both Gx(cid:2) and Gx (anything else would again be inconsistent with the assumption that Gx is projective). Therefore, we can construct a transition sequence C0,m such that Gcm = Gx, by starting in c0 = cs(x) and applying p exactly the same q transitions as in C0,q, followed by as many LEFT-ARC l transitions as there are left dependents of p in Gx, interleaving NO-ARCp transitions whenever the p node at the head of λ1 already has a head, followed by a RIGHT-ARC l transition if p has a head in Gx. One ﬁnal SHIFTn transition takes us to the terminal conﬁguration cm = (λcm, [ ], [ ], Ax). (cid:1) Theorem 11 The worst-case time complexity of the projective, list-based algorithm is O(n2), where n is the length of the input sentence. Proof 11 Same as Proof 8. (cid:1) Theorem 12 The worst-case space complexity of the projective, list-based algorithm is O(n), where n is the length of the input sentence. Proof 12 Same as Proof 9. (cid:1) 6. Experimental Evaluation We have deﬁned four different transition systems for incremental dependency parsing, proven their correctness for different classes of dependency graphs, and analyzed their time and space complexity under the assumption that there exists a constant-time oracle for predicting the next transition. In this section, we present an experimental evaluation of the accuracy and efﬁciency that can be achieved with these systems in deterministic data-driven parsing, that is, when the oracle is approximated by a classiﬁer trained on treebank data. The purpose of the evaluation is to compare the performance of the four algorithms under realistic conditions, thereby complementing the purely formal analysis presented so far. The purpose is not to produce state-of-the-art results for all algorithms on the data sets used, which would require extensive experimentation and optimization going well beyond the limits of this study. 6.1 Experimental Setup The data sets used are taken from the CoNLL-X shared task on multilingual dependency parsing (Buchholz and Marsi 2006). We have used all the available data sets, taken 537 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Computational Linguistics Volume 34, Number 4 Table 1 Data sets. Tok = number of tokens (×1000); Sen = number of sentences (×1000); T/S = tokens per sentence (mean); Lem = lemmatization present; CPoS = number of coarse-grained part-of-speech tags; PoS = number of (ﬁne-grained) part-of-speech tags; MSF = number of morphosyntactic features (split into atoms); Dep = number of dependency types; NPT = proportion of non-projective dependencies/tokens (%); NPS = proportion of non-projective dependency graphs/sentences (%). Language Tok Sen T/S Lem CPoS PoS MSF Dep NPT NPS Arabic Bulgarian Chinese Czech Danish Dutch German Japanese Portuguese Slovene Spanish Swedish Turkish 54 94 1.5 37.2 yes 190 14.4 14.8 no 5.9 no 337 57.0 1,249 72.7 17.2 yes 5.2 18.2 no 195 13.3 14.6 yes 700 39.2 17.8 no 151 17.0 8.9 no 9.1 22.8 yes 207 1.5 18.7 yes 29 89 3.3 27.0 yes 191 11.0 17.3 no 5.0 11.5 yes 58 14 11 22 12 10 13 52 20 15 11 15 37 14 19 53 303 63 24 302 52 77 21 28 38 37 30 19 50 0 61 47 81 0 0 146 51 33 0 82 27 18 82 78 52 26 46 7 55 25 21 56 25 0.4 0.4 0.0 1.9 1.0 5.4 2.3 1.1 1.3 1.9 0.1 1.0 1.5 11.2 5.4 0.0 23.2 15.6 36.4 27.8 5.3 18.9 22.2 1.7 9.8 11.6 from treebanks of thirteen different languages with considerable typological variation. Table 1 gives an overview of the training data available for each language. For data sets that include a non-negligible proportion of non-projective dependency graphs, it can be expected that the non-projective list-based algorithm will achieve higher accuracy than the strictly projective algorithms. In order to make the comparison more fair, we therefore also evaluate pseudo-projective versions of the latter algorithms, making use of graph transformations in pre- and post-processing to recover non- projective dependency arcs, following Nivre and Nilsson (2005). For each language, seven different parsers were therefore trained as follows: For the non-projective list-based algorithm, one parser was trained without preprocessing the training data. For the three projective algorithms, two parsers were trained after preprocessing the training data as follows: For the strictly projective parser, non-projective dependency graphs in the training data were transformed by lifting non-projective arcs to the nearest permissible ancestor of the real head. This corresponds to the Baseline condition in Nivre and Nilsson (2005). For the pseudo-projective parser, non-projective dependency graphs in the training data were transformed by lifting non-projective arcs to the nearest permissible ancestor of the real head, and augmenting the arc label with the label of the real head. The output of this parser was post-processed by lowering dependency arcs with augmented labels using a top-down, left-to-right, breadth-ﬁrst search for the ﬁrst descendant of the head that matches the augmented arc label. This corresponds to the Head condition in Nivre and Nilsson (2005). 1. 2. (a) (b) 538 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Nivre Deterministic Incremental Dependency Parsing Table 2 Feature models. Rows represent tokens deﬁned relative to the current conﬁguration (L[i] = ith element of list/stack L of length n; hd(x) = head of x; ld(x) = leftmost dependent of x; rd(x) = rightmost dependent of x). Columns represent attributes of tokens (Form = word form; Lem = lemma; CPoS = coarse part-of-speech; FPoS = ﬁne part-of-speech; Feats = morphosyntactic features; Dep = dependency label). Filled cells represent features used by one or more algorithms (All = all algorithms; S = arc-standard, stack-based; E = arc-eager, stack-based; N = non-projective, list-based; P = projective, list-based). Attributes Tokens Form Lem CPoS FPoS Feats Dep All All All All SE SE SE E β[0] β[1] β[2] β[3] ld(β[0]) rd(β[0]) σ[0] σ[1] hd(σ[0]) ld(σ[0]) rd(σ[0]) All All All All SE SE All N All S SE E SE SE l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / NP NP NP NP NP NP λ1[0] λ1[1] hd(λ1[0]) NP ld(λ1[0]) rd(λ1[0]) λ2[0] λ2[n] NP N N NP NP All parsers were trained using the freely available MaltParser system,6 which provides implementations of all the algorithms described in Sections 4 and 5. MaltParser also incorporates the LIBSVM library for support vector machines (Chang and Lin 2001), which was used to train classiﬁers for predicting the next transition. Training data for the classiﬁers were generated by parsing each sentence in the training set using the gold- standard dependency graph as an oracle. For each transition t(c) in the oracle parse, a training instance (Φ(c), t) was created, where Φ(c) is a feature vector representation of the parser conﬁguration c. Because the purpose of the experiments was not to optimize parsing accuracy as such, no work was done on feature selection for the different algorithms and languages. Instead, all parsers use a variant of the simple feature model used for parsing English and Swedish in Nivre (2006b), with minor modiﬁcations to suit the different algorithms. Table 2 shows the feature sets used for different parsing algorithms.7 Each row represents a node deﬁned relative to the current parser conﬁguration, where nodes 6 Available at http://w3.msi.vxu.se/users/nivre/research/MaltParser.html. 7 For each of the three projective algorithms, the strictly projective and the pseudo-projective variants used exactly the same set of features, although the set of values for the dependency label features were different because of the augmented label set introduced by the pseudo-projective technique. 539 / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Computational Linguistics Volume 34, Number 4 deﬁned relative to the stack σ are only relevant for stack-based algorithms, whereas nodes deﬁned relative to the lists λ1 and λ2 are only relevant for list-based algorithms. We use the notation L[i], for arbitrary lists or stacks, to denote the ith element of L, with L[0] for the ﬁrst element (top element of a stack) and L[n] for the last element. Nodes deﬁned relative to the partially-built dependency graph make use of the operators hd, ld, and rd, which return, respectively, the head, the leftmost dependent, and the rightmost dependent of a node in the dependency graph Gc deﬁned by the current conﬁguration c, if such a node exists, and a null value otherwise. The columns in Table 2 represent attributes of nodes (tokens) in the input (word form, lemma, coarse part-of-speech, ﬁne part-of-speech, morphosyntactic features) or in the partially-built dependency graph (dependency label), which can be used to deﬁne features. Each cell in the table thus represents a feature fij = aj(ni), deﬁned by selecting the attribute aj in the jth column from the node ni characterized in the ith row. For example, the feature f11 is the word form of the ﬁrst input node (token) in the buffer β. The symbols occurring in ﬁlled cells indicate for which parsing algorithms the feature is active, where S stands for arc- standard stack-based, E for arc-eager stack-based, N for non-projective list-based, and P for projective list-based. Features that are used for some but not all algorithms are typically not meaningful for all algorithms. For example, a right dependent of the ﬁrst node in the buffer β can only exist (at decision time) when using the arc-standard stack- based algorithm. Hence, this feature is inactive for all other algorithms. The SVM classiﬁers were trained with a quadratic kernel K(xi, xj) = (γxT i xj + r)2 and LIBSVM’s built-in one-versus-one strategy for multi-class classiﬁcation, convert- ing symbolic features to numerical ones using the standard technique of binarization. The parameter settings were γ = 0.2 and r = 0 for the kernel parameters, C = 0.5 for the penalty parameter, and (cid:8) = 1.0 for the termination criterion. These settings were extrapolated from many previous experiments under similar conditions, using cross- validation or held-out subsets of the training data for tuning, but in these experiments they were kept ﬁxed for all parsers and languages. In order to reduce training times, the set of training instances derived from a given training set was split into smaller sets, for which separate multi-class classiﬁers were trained, using FPoS(β[0]), that is, the (ﬁne- grained) part of speech of the ﬁrst node in the buffer, as the deﬁning feature for the split. The seven different parsers for each language were evaluated by running them on the dedicated test set from the CoNLL-X shared task, which consists of approximately 5,000 tokens for all languages. Because the dependency graphs in the gold standard are always trees, each output graph was converted, if necessary, from a forest to a tree by attaching every root node i (i > 0) to the special root node 0 with a default label
ROOT. Parsing accuracy was measured by the labeled attachment score (LAS), questo è,
the percentage of tokens that are assigned the correct head and dependency label, COME
well as the unlabeled attachment score (UAS), questo è, the percentage of tokens with
the correct head, and the label accuracy (LA), questo è, the percentage of tokens with the
correct dependency label. All scores were computed with the scoring software from
the CoNLL-X shared task, eval.pl, with default settings. This means that punctuation
tokens are excluded in all scores. In addition to parsing accuracy, we evaluated
efﬁciency by measuring the learning time and parsing time in seconds for each data set.
Before turning to the results of the evaluation, we need to fulﬁll the promise from
Remarks 1 E 2 to discuss the way in which treebank-induced classiﬁers approximate
oracles and to what extent they satisfy the condition of constant-time operation that
was assumed in all the results on time complexity in Sections 4 E 5. When pre-
dicting the next transition at run-time, there are two different computations that take

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Nivre

Deterministic Incremental Dependency Parsing

place: the ﬁrst is the classiﬁer returning a transition t as the output class for an input
feature vector Φ(C), and the second is a check whether the preconditions of t are satisﬁed
in c. If the preconditions are satisﬁed, the transition t is performed; otherwise a default
transition (with no preconditions) is performed instead.8 (The default transition is SHIFT
for the stack-based algorithms and NO-ARC for the list-based algorithms.) The time
required to compute the classiﬁcation t of Φ(C) depends on properties of the classiﬁer,
such as the number of support vectors and the number of classes for a multi-class SVM
classiﬁer, but is independent of the length of the input and can therefore be regarded
as a constant as far as the time complexity of the parsing algorithm is concerned.9
The check of preconditions is a trivial constant-time operation in all cases except one,
namely the need to check whether there is a path between two nodes for the LEFT-ARCn
l
and RIGHT-ARCn
l transitions of the non-projective list-based algorithm. Maintaining the
information needed for this check and updating it with each addition of a new arc
to the graph is equivalent to the union-ﬁnd operations for disjoint set data structures.
Using the techniques of path compression and union by rank, the amortized time per
operation is O(α(N)) per operation, where n is the number of elements (nodes in this
case) and α(N) is the inverse of the Ackermann function, which means that α(N) È
less than 5 for all remotely practical values of n and is effectively a small constant
(Cormen, Leiserson, and Rivest 1990). With this proviso, all the complexity results from
Sezioni 4 E 5 can be regarded as valid also for the classiﬁer-based implementation of
deterministic, incremental dependency parsing.

6.2 Parsing Accuracy

Tavolo 3 shows the parsing accuracy obtained for each of the 7 parsers on each of the
13 languages, as well as the average over all languages, with the top score in each
row set in boldface. For comparison, we also include the results of the two top scoring
systems in the CoNLL-X shared task, those of McDonald, Lerman, and Pereira (2006)
and Nivre et al. (2006). Starting with the LAS, we see that the multilingual average
is very similar across the seven parsers, with a difference of only 0.58 percentage
points between the best and the worst result, obtained with the non-projective and
the strictly projective version of the list-based parser, rispettivamente. Tuttavia, given the
large amount of data, some of the differences are nevertheless statistically signiﬁcant
(according to McNemar’s test, α = .05). Broadly speaking, the group consisting of the
non-projective, list-based parser and the three pseudo-projective parsers signiﬁcantly
outperforms the group consisting of the three projective parsers, whereas there are
no signiﬁcant differences within the two groups.10 This shows that the capacity to
capture non-projective dependencies does make a signiﬁcant difference, even though
such dependencies are infrequent in most languages.

The best result is about one percentage point below the top scores from the original
CoNLL-X shared task, but it must be remembered that the results in this article have

8 A more sophisticated strategy would be to back off to the second best choice of the oracle, assuming that
the oracle provides a ranking of all the possible transitions. On the whole, Tuttavia, classiﬁers very rarely
predict transitions that are not legal in the current conﬁguration.

9 The role of this constant in determining the overall running time is similar to that of a grammar constant

in grammar-based parsing.

10 The only exception to this generalization is the pseudo-projective, list-based parser, which is signiﬁcantly

worse than the non-projective, list-based parser, but not signiﬁcantly better than the projective,
arc-standard, stack-based parser.

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Linguistica computazionale

Volume 34, Numero 4

Tavolo 3
Parsing accuracy for 7 parsers on 13 languages, measured by labeled attachment score (LAS),
unlabeled attachment score (UAS) and label accuracy (LA). NP-L = non-projective list-based;
P-L = projective list-based; PP-L = pseudo-projective list-based; P-E = projective arc-eager
stack-based; PP-E = pseudo-projective arc-eager stack-based; P-S = projective arc-standard
stack-based; PP-S = pseudo-projective arc-standard stack-based; McD = McDonald, Lerman
and Pereira (2006); Niv = Nivre et al. (2006).

Labeled Attachment Score (LAS)

Language NP-L P-L PP-L P-E PP-E P-S PP-S McD Niv

63.25 63.19 63.13 64.93 64.95 65.79 66.05 66.91 66.71
Arabic
87.79 87.75 87.39 87.75 87.41 86.42 86.71 87.57 87.41
Bulgarian
85.77 85.96 85.96 85.96 85.96 86.00 86.00 85.90 86.92
Chinese
78.12 76.24 78.04 76.34 77.46 78.18 80.12 80.18 78.42
Czech
84.59 84.15 84.35 84.25 84.45 84.17 84.15 84.79 84.77
Danish
77.41 74.71 76.95 74.79 76.89 73.27 74.79 79.19 78.59
Dutch
84.42 84.21 84.38 84.23 84.46 84.58 84.58 87.34 85.82
German
90.97 90.57 90.53 90.83 90.89 90.59 90.63 90.71 91.65
Japanese
Portuguese 86.70 85.91 86.20 85.83 86.12 85.39 86.09 86.82 87.60
70.06 69.88 70.12 69.50 70.22 72.00 71.88 73.44 70.30
Slovene
80.18 79.80 79.60 79.84 79.60 78.70 78.42 82.25 81.29
Spanish
83.03 82.63 82.41 82.63 82.41 82.12 81.54 82.55 84.58
Swedish
64.69 64.49 64.37 64.37 64.43 64.67 64.73 63.19 65.68
Turkish

Average

79.77 79.19 79.49 79.33 79.63 79.38 79.67 80.83 80.75

Unlabeled Attachment Score (UAS)

Language NP-L P-L PP-L P-E PP-E P-S PP-S McD Niv

76.43 76.19 76.49 76.31 76.25 77.76 77.98 79.34 77.52
Arabic
91.68 91.92 91.62 91.92 91.64 90.80 91.10 92.04 91.72
Bulgarian
89.42 90.24 90.24 90.24 90.24 90.42 90.42 91.07 90.54
Chinese
84.88 82.82 84.58 82.58 83.66 84.50 86.44 87.30 84.80
Czech
89.76 89.40 89.42 89.52 89.52 89.26 89.38 90.58 89.80
Danish
80.25 77.29 79.59 77.25 79.47 75.95 78.03 83.57 81.35
Dutch
87.80 87.10 87.38 87.14 87.42 87.46 87.44 90.38 88.76
German
92.64 92.60 92.56 92.80 92.72 92.50 92.54 92.84 93.10
Japanese
Portuguese 90.56 89.82 90.14 89.74 90.08 89.00 89.64 91.36 91.22
79.06 78.78 79.06 78.42 78.98 80.72 80.76 83.17 78.72
Slovene
83.39 83.75 83.65 83.79 83.65 82.35 82.13 86.05 84.67
Spanish
89.54 89.30 89.03 89.30 89.03 88.81 88.39 88.93 89.50
Swedish
75.12 75.24 75.02 75.32 74.81 75.94 75.76 74.67 75.82
Turkish

Average

85.43 84.96 85.29 84.95 85.19 85.04 85.39 87.02 85.96

Label Accuracy (LA)

Language NP-L P-L PP-L P-E PP-E P-S PP-S McD Niv

75.93 76.15 76.09 78.46 78.04 79.30 79.10 79.50 80.34
Arabic
90.68 90.68 90.40 90.68 90.42 89.53 89.81 90.70 90.44
Bulgarian
88.01 87.95 87.95 87.95 87.95 88.33 88.33 88.23 89.01
Chinese
84.54 84.54 84.42 84.80 84.66 86.00 86.58 86.72 85.40
Czech
88.90 88.60 88.76 88.62 88.82 89.00 88.98 89.22 89.16
Danish
82.43 82.59 82.13 82.57 82.15 80.71 80.13 83.89 83.69
Dutch
89.74 90.08 89.92 90.06 89.94 90.79 90.36 92.11 91.03
German
93.54 93.14 93.14 93.54 93.56 93.12 93.18 93.74 93.34
Japanese
Portuguese 90.56 90.54 90.34 90.46 90.26 90.42 90.26 90.46 91.54
78.88 79.70 79.40 79.32 79.48 81.61 81.37 82.51 80.54
Slovene
89.46 89.04 88.66 89.06 88.66 88.30 88.12 90.40 90.06
Spanish
85.50 85.40 84.92 85.40 84.92 84.66 84.01 85.58 87.39
Swedish
77.79 77.32 77.02 77.00 77.39 77.02 77.20 77.45 78.49
Turkish

Average

85.84 85.83 85.63 85.99 85.87 86.06 85.96 86.96 87.03

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Nivre

Deterministic Incremental Dependency Parsing

been obtained without optimization of feature representations or learning algorithm
parameters. The net effect of this can be seen in the result for the pseudo-projective
version of the arc-eager, stack-based parser, which is identical to the system used by
Nivre et al. (2006), except for the lack of optimization, and which suffers a loss of 1.12
percentage points overall.

The results for UAS show basically the same pattern as the LAS results, but with
even less variation between the parsers. Nevertheless, there is still a statistically sig-
niﬁcant margin between the non-projective, list-based parser and the three pseudo-
projective parsers, on the one hand, and the strictly projective parsers, on the other.11
For label accuracy (LA), ﬁnally, the most noteworthy result is that the strictly projec-
tive parsers consistently outperform their pseudo-projective counterparts, although the
difference is statistically signiﬁcant only for the projective, list-based parser. This can
be explained by the fact that the pseudo-projective parsing technique increases the
number of distinct dependency labels, using labels to distinguish not only between
different syntactic functions but also between “lifted” and “unlifted” arcs. It is there-
fore understandable that the pseudo-projective parsers suffer a drop in pure labeling
accuracy.

Despite the very similar performance of all parsers on average over all languages,
there are interesting differences for individual languages and groups of languages.
These differences concern the impact of non-projective, pseudo-projective, and strictly
projective parsing, on the one hand, and the effect of adopting an arc-eager or an arc-
standard parsing strategy for the stack-based parsers, on the other. Before we turn
to the evaluation of efﬁciency, we will try to analyze some of these differences in a
little more detail, starting with the different techniques for capturing non-projective
dependencies.

First of all, we may observe that the non-projective, list-based parser outperforms its
strictly projective counterpart for all languages except Chinese. The result for Chinese
is expected, given that it is the only data set that does not contain any non-projective
dependencies, but the difference in accuracy is very slight (0.19 percentage points).
Così, it seems that the non-projective parser can also be used without loss in accuracy
for languages with very few non-projective structures. The relative improvement in
accuracy for the non-projective parser appears to be roughly linear in the percent-
age of non-projective dependencies found in the data set, with a highly signiﬁcant
correlation (Pearson’s r = 0.815, p = 0.0007). The only language that clearly diverges
from this trend is German, where the relative improvement is much smaller than
expected.

If we compare the non-projective, list-based parser to the strictly projective stack-
based parsers, we see essentially the same pattern but with a little more variation. For
the arc-eager, stack-based parser, the only anomaly is the result for Arabic, che è
signiﬁcantly higher than the result for the non-projective parser, but this seems to be
due to a particularly bad performance of the list-based parsers as a group for this
language.12 For the arc-standard, stack-based parser, the data is considerably more
noisy, which is related to the fact that the arc-standard parser in itself has a higher

11 The exception this time is the pseudo-projective, arc-eager parser, which has a statistically signiﬁcant
difference up to the non-projective parser but a non-signiﬁcant difference down to the projective,
arc-standard parser.

12 A possible explanation for this result is the extremely high average sentence length for Arabic, Quale

leads to a greater increase in the number of potential arcs considered for the list-based parsers than for
the stack-based parsers.

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Linguistica computazionale

Volume 34, Numero 4

variance than the other parsers, an observation that we will return to later on. Ancora, IL
correlation between relative improvement in accuracy and percentage of non-projective
dependencies is signiﬁcant for both the arc-eager parser (r = 0.766, p = 0.001) and the
arc-standard parser (r = 0.571, p = 0.02), although clearly not as strong as for the list-
based parser. It therefore seems reasonable to conclude that the non-projective parser in
general can be expected to outperform a strictly projective parser with a margin that is
directly related to the proportion of non-projective dependencies in the data.

Having compared the non-projective, list-based parser to the strictly projective
parsers, we will now scrutinize the results obtained when coupling the projective
parsers with the pseudo-projective parsing technique, as an alternative method for
capturing non-projective dependencies. The overall pattern is that pseudo-projective
parsing improves the accuracy of a projective parser for languages with more than 1%
of non-projective dependencies, as seen from the results for Czech, Dutch, German, E
Portuguese. For these languages, the pseudo-projective parser is never outperformed
by its strictly projective counterpart, and usually does considerably better, although the
improvements for German are again smaller than expected. For Slovene and Turkish,
we ﬁnd improvement only for two out of three parsers, despite a relatively high share
of non-projective dependencies (1.9% for Slovene, 1.5% for Turkish). Given that Slovene
and Turkish have the smallest training data sets of all languages, this is consistent with
previous studies showing that pseudo-projective parsing is sensitive to data sparseness
(Nilsson, Nivre, and Hall 2007). For languages with a lower percentage of non-projective
dependencies, the pseudo-projective technique seems to hurt performance more often
than not, possibly as a result of decreasing the labeling accuracy, as noted previously.
It is worth noting that Chinese is a special case in this respect. Because there are no
non-projective dependencies in this data set, the projectivized training data set will be
identical to the original one, which means that the pseudo-projective parser will behave
exactly as the projective one.

Comparing non-projective parsing to pseudo-projective parsing, it seems clear that
both can improve parsing accuracy in the presence of signiﬁcant amounts of non-
projective dependencies, but the former appears to be more stable in that it seldom
or never hurts performance, whereas the latter can be expected to have a negative effect
on accuracy when the amount of training data or non-projective dependencies (or both)
is not high enough. Inoltre, the non-projective parser tends to outperform the best
pseudo-projective parsers, both on average and for individual languages. Infatti, IL
pseudo-projective technique outperforms the non-projective parser only in combination
with the arc-standard, stack-based parsing algorithm, and this seems to be due more to
the arc-standard parsing strategy than to the pseudo-projective technique as such. IL
relevant question here is therefore why arc-standard parsing seems to work particularly
well for some languages, with or without pseudo-projective parsing.

Going through the results for individual languages, it is clear that the arc-standard
algorithm has a higher variance than the other algorithms. For Bulgarian, Dutch, E
Spanish, the accuracy is considerably lower than for the other algorithms, in most
cases by more than one percentage point. But for Arabic, Czech, and Slovene, we ﬁnd
exactly the opposite pattern, with the arc-standard parsers sometimes outperforming
the other parsers by more than two percentage points. For the remaining languages,
the arc-standard algorithm performs on a par with the other algorithms.13 In order to

13 The arc-standard algorithm achieves the highest score also for Chinese, German, and Turkish, but in

these cases only by a small margin.

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Deterministic Incremental Dependency Parsing

explain this pattern we need to consider the way in which properties of the algorithms
interact with properties of different languages and the way they have been annotated
syntactically.

First of all, it is important to note that the two list-based algorithms and the arc-
eager variant of the stack-based algorithm are all arc-eager in the sense that an arc (io, l, j)
is always added at the earliest possible moment, questo è, in the ﬁrst conﬁguration where
i and j are the target tokens. For the arc-standard stack-based parser, this is still true for
left dependents (cioè., arcs (io, l, j) such that j < i) but not for right dependents, where an arc (i, l, j) (i < j) should be added only at the point where all arcs of the form (j, l(cid:2), k) have already been added (i.e., when the dependent j has already found all its dependents). This explains why the results for the two list-based parsers and the arc-eager stack- based parser are so well correlated, but it does not explain why the arc-standard strategy works better for some languages but not for others. The arc-eager strategy has an advantage in that a right dependent j can be attached to its head i at any time without having to decide whether j itself should have a right dependent. By contrast, with the arc-standard strategy it is necessary to decide not only whether j is a right dependent of i but also whether it should be added now or later, which means that two types of errors are possible even when the decision to attach j to i is correct. Attaching too early means that right dependents can never be attached to j; postponing the attachment too long means that j will never be added to i. None of these errors can occur with the arc-eager strategy, which therefore can be expected to work better for data sets where this kind of “ambiguity” is commonly found. In order for this to be the case, there must ﬁrst of all be a signiﬁcant proportion of left-headed structures in the data. Thus, we ﬁnd that in all the data sets for which the arc-standard parsers do badly, the percentage of left-headed dependencies is in the 50–75% range. However, it must also be pointed out that the highest percentage of all is found in Arabic (82.9%), which means that a high proportion of left-headed structures may be a necessary but not sufﬁcient condition for the arc-eager strategy to work better than the arc-standard strategy. We conjecture that an additional necessary condition is an annotation style that favors more deeply embedded structures, giving rise to chains of left-headed structures where each node is dependent on the preceding one, which increases the number of points at which an incorrect decision can be made by an arc- standard parser. However, we have not yet fully veriﬁed the extent to which this condi- tion holds for all the data sets where the arc-eager parsers outperform their arc-standard counterparts. Although the arc-eager strategy has an advantage in that the decisions involved in attaching a right dependent are simpler, it has the disadvantage that it has to commit early. This may either lead the parser to add an arc (i, l, j) (i < j) when it is not correct to do so, or fail to add the same arc in a situation where it should have been added, in both cases because the information available at an early point makes the wrong decision look probable. In the ﬁrst case, the arc-standard parser may still get the analysis right, if it also seems probable that j should have a right dependent (in which case it will postpone the attachment); in the second case, it may get a second chance to add the arc if it in fact adds a right dependent to j at a later point. It is not so easy to predict what type of structures and annotation will favor the arc-standard parser in this way, but it is likely that having many right dependents attached to (or near) the root could cause problems for the arc-eager algorithms, since these dependencies determine the global structure and often span long distances, which makes it harder to make correct decisions early in the parsing process. This is consistent with earlier studies showing that parsers using the arc-eager, stack-based algorithm tend to predict dependents of the root with 545 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Computational Linguistics Volume 34, Number 4 lower precision than other algorithms.14 Interestingly, the three languages for which the arc-standard parser has the highest improvement (Arabic, Czech, Slovene) have a very similar annotation, based on the Prague school tradition of dependency grammar, which not only allows multiple dependents of the root but also uses several different labels for these dependents, which means that they will be analyzed correctly only if a RIGHT-ARC transition is performed with the right label at exactly the right point in time. This is in contrast to annotation schemes that use a default label ROOT, for dependents of the root, where such dependents can often be correctly recovered in post-processing by attaching all remaining roots to the special root node with the default label. We can see the effect of this by comparing the two stack-based parsers (in their pseudo- projective versions) with respect to precision and recall for the dependency type PRED (predicate), which is the most important label for dependents of the root in the data sets for Arabic, Czech, and Slovene. While the arc-standard parser has 78.02% precision and 70.22% recall, averaged over the three languages, the corresponding ﬁgures for the arc- eager parser are as low as 68.93% and 65.93%, respectively, which represents a drop of almost ten percentage points in precision and almost ﬁve percentage points in recall. Summarizing the results of the accuracy evaluation, we have seen that all four algo- rithms can be used for deterministic, classiﬁer-based parsing with competitive accuracy. The results presented are close to the state of the art without any optimization of feature representations and learning algorithm parameters. Comparing different algorithms, we have seen that the capacity to capture non-projective dependencies makes a signif- icant difference in general, but with language-speciﬁc effects that depend primarily on the frequency of non-projective constructions. We have also seen that the non-projective list-based algorithm is more stable and predictable in this respect, compared to the use of pseudo-projective parsing in combination with an essentially projective parsing algo- rithm. Finally, we have observed quite strong language-speciﬁc effects for the difference between arc-standard and arc-eager parsing for the stack-based algorithms, effects that can be tied to differences in linguistic structure and annotation style between different data sets, although a much more detailed error analysis is needed before we can draw precise conclusions about the relative merits of different parsing algorithms for different languages and syntactic representations. 6.3 Efﬁciency Before we consider the evaluation of efﬁciency in both learning and parsing, it is worth pointing out that the results will be heavily dependent on the choice of support vector machines for classiﬁcation, and cannot be directly generalized to the use of deterministic incremental parsing algorithms together with other kinds of classiﬁers. However, because support vector machines constitute the state of the art in classiﬁer- based parsing, it is still worth examining how learning and parsing times vary with the parsing algorithm while parameters of learning and classiﬁcation are kept constant. Table 4 gives the results of the efﬁciency evaluation. Looking ﬁrst at learning times, it is obvious that learning time depends primarily on the number of training instances, which is why we can observe a difference of several orders of magnitude in learning time between the biggest training set (Czech) and the smallest training set (Slovene) 14 This is shown by Nivre and Scholz (2004) in comparison to the iterative, arc-standard algorithm of Yamada and Matsumoto (2003) and by McDonald and Nivre (2007) in comparison to the spanning tree algorithm of McDonald, Lerman, and Pereira (2006). 546 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Nivre Deterministic Incremental Dependency Parsing Table 4 Learning and parsing time for seven parsers on six languages, measured in seconds. NP-L = non-projective list-based; P-L = projective list-based; PP-L = pseudo-projective list-based; P-E = projective arc-eager stack-based; PP-E = pseudo-projective arc-eager stack-based; P-S = projective arc-standard stack-based; PP-S = pseudo-projective arc-standard stack-based. Language NP-L P-L PP-L P-E PP-E P-S PP-S Learning Time Arabic Bulgarian Chinese Czech Danish Dutch German Japanese Portuguese Slovene Spanish Swedish Turkish 614 2,918 13,019 603 2,926 13,019 647 2,939 13,029 1,639 3,321 13,705 1,636 650 1,814 3,391 2,919 6,796 17,034 13,705 13,029 546,880 250,560 248,511 279,586 280,069 407,673 406,857 647 1,246 6,812 3,055 24,705 16,899 199 203 7,731 8,436 90 93 959 565 1,402 945 527 504 643 7,000 24,402 199 7,724 86 960 1,350 515 1,262 2,965 17,601 208 8,335 99 565 1,022 516 1,260 2,966 17,600 188 8,336 90 566 1,020 519 1,248 3,039 16,874 191 8,433 78 562 942 498 2,964 7,701 48,699 211 25,621 167 1,999 2,410 720 Average 105,713 46,849 46,616 51,695 51,876 74,798 74,691 Parsing Time Language NP-L P-L PP-L P-E PP-E P-S PP-S Arabic Bulgarian Chinese Czech Danish Dutch German Japanese Portuguese Slovene Spanish Swedish Turkish 213 139 1,008 5,244 109 349 781 10 670 69 133 286 218 103 93 855 3,043 66 209 456 8 298 44 67 202 162 131 102 855 5,889 83 362 947 8 494 62 75 391 398 108 93 855 3,460 66 211 455 9 298 47 67 201 162 135 103 855 6,701 83 363 945 10 493 65 75 391 403 196 135 803 3,874 82 253 494 7 437 43 80 242 153 243 147 803 7,437 106 405 1,004 7 717 64 91 456 380 Average 1,240 712 1,361 782 1,496 897 1,688 for a given parsing algorithm. Broadly speaking, for any given parsing algorithm, the ranking of languages with respect to learning time follows the ranking with respect to training set size, with a few noticeable exceptions. Thus, learning times are shorter than expected, relative to other languages, for Swedish and Japanese, but longer than expected for Arabic and (except in the case of the arc-standard parsers) for Danish. However, the number of training instances for the SVM learner depends not only on the number of tokens in the training set, but also on the number of transitions required to parse a sentence of length n. This explains why the non-projective list-based algorithm, with its quadratic complexity, consistently has longer learning times than the linear stack-based algorithms. However, it can also be noted that the projective, list- based algorithm, despite having the same worst-case complexity as the non-projective 547 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Computational Linguistics Volume 34, Number 4 algorithm, in practice behaves much more like the arc-eager stack-based algorithm and in fact has a slightly lower learning time than the latter on average. The arc-standard stack-based algorithm, ﬁnally, again shows much more variation than the other algo- rithms. On average, it is slower to train than the arc-eager algorithm, and sometimes very substantially so, but for a few languages (Danish, Japanese, Portuguese, Slovene) it is actually faster (and considerably so for Danish). This again shows that learning time depends on other properties of the training sets than sheer size, and that some data sets may be more easily separable for the SVM learner with one parsing algorithm than with another. It is noteworthy that there are no consistent differences in learning time between the strictly projective parsers and their pseudo-projective counterparts, despite the fact that the pseudo-projective technique increases the number of distinct classes (because of its augmented arc labels), which in turn increases the number of binary classiﬁers that need to be trained in order to perform multi-class classiﬁcation with the one-versus-one method. The number of classiﬁers is m(m−1) , where m is the number of classes, and the pseudo-projective technique with the encoding scheme used here can theoretically lead to a quadratic increase in the number of classes. The fact that this has no noticeable effect on efﬁciency indicates that learning time is dominated by other factors, in particular the number of training instances. 2 Turning to parsing efﬁciency, we may ﬁrst note that parsing time is also dependent on the size of the training set, through a dependence on the number of support vectors, which tend to grow with the size of the training set. Thus, for any given algorithm, there is a strong tendency that parsing times for different languages follow the same order as training set sizes. The notable exceptions are Arabic, Turkish, and Chinese, which have higher parsing times than expected (relative to other languages), and Japanese, where parsing is surprisingly fast. Because these deviations are the same for all algorithms, it seems likely that they are related to speciﬁc properties of these data sets. It is also worth noting that for Arabic and Japanese the deviations are consistent across learning and parsing (slower than expected for Arabic, faster than expected for Japanese), whereas for Chinese there is no consistent trend (faster than expected in learning, slower than expected in parsing). Comparing algorithms, we see that the non-projective list-based algorithm is slower than the strictly projective stack-based algorithms, which can be expected from the difference in time complexity. But we also see that the projective list-based algorithm, despite having the same worst-case complexity as the non-projective algorithm, in practice behaves like the linear-time algorithms and is in fact slightly faster on average than the arc-eager stack-based algorithm, which in turn outperforms the arc-standard stack-based algorithm. This is consistent with the results from oracle parsing reported in Nivre (2006a), which show that, with the constraint of projectivity, the relation between sentence length and number of transitions for the list-based parser can be regarded as linear in practice. Comparing the arc-eager and the arc-standard variants of the stack-based algorithm, we ﬁnd the same kind of pattern as for learning time in that the arc-eager parser is faster for all except a small set of languages: Chinese, Japanese, Slovene, and Turkish. Only two of these, Japanese and Slovene, are languages for which learning is also faster with the stack-based algorithm, which again shows that there is no straightforward correspondence between learning time and parsing time. Perhaps the most interesting result of all, as far as efﬁciency is concerned, is to be found in the often dramatic differences in parsing time between the strictly projective parsers and their pseudo-projective counterparts. Although we did not see any clear effect of the increased number of classes, hence classiﬁers, on learning time earlier, it is 548 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Nivre Deterministic Incremental Dependency Parsing quite clear that there is a noticeable effect on parsing time, with the pseudo-projective parsers always being substantially slower. In fact, in some cases the pseudo-projective parsers are also slower than the non-projective list-based parser, despite the difference in time complexity that exists at least for the stack-based parsers. This result holds on average over all languages and for ﬁve out of thirteen of the individual languages and shows that the advantage of linear-time parsing complexity (for the stack-based parsers) can be outweighed by the disadvantage of a more complex classiﬁcation problem in pseudo-projective parsing. In other words, the larger constant associated with a larger cohort of SVM classiﬁers for the pseudo-projective parser can be more important than the better asymptotic complexity of the linear-time algorithm in the range of sentence lengths typically found in natural language. Looking more closely at the variation in sentence length across languages, we ﬁnd that the pseudo-projective parsers are faster than the non-projective parser for all data sets with an average sentence length above 18. For data sets with shorter sentences, the non-projective parser is more efﬁcient in all except three cases: Bulgarian, Chinese, and Japanese. For Chinese this is easily explained by the absence of non-projective dependencies, making the performance of the pseudo- projective parsers identical to their strictly projective counterparts. For the other two languages, the low number of distinct dependency labels for Japanese and the low per- centage of non-projective dependencies for Bulgarian are factors that mitigate the effect of enlarging the set of dependency labels in pseudo-projective parsing. We conclude that the relative efﬁciency of non-projective and pseudo-projective parsing depends on several factors, of which sentence length appears to be the most important, but where the number of distinct dependency labels and the percentage of non-projective dependencies also play a role. 7. Related Work Data-driven dependency parsing using supervised machine learning was pioneered by Eisner (1996), who showed how traditional chart parsing techniques could be adapted for dependency parsing to give efﬁcient parsing with exact inference over a probabilistic model where the score of a dependency tree is the sum of the scores of individual arcs. This approach has been further developed in particular by Ryan McDonald and his colleagues (McDonald, Crammer, and Pereira 2005; McDonald et al. 2005; McDonald and Pereira 2006) and is now known as spanning tree parsing, because the problem of ﬁnding the most probable tree under this type of model is equivalent to ﬁnding an optimum spanning tree in a dense graph containing all possible dependency arcs. If we assume that the score of an individual arc is independent of all other arcs, this problem can be solved efﬁciently for arbitrary non-projective dependency trees using the Chu-Liu-Edmonds algorithm, as shown by McDonald et al. (2005). Spanning tree algorithms have so far primarily been combined with online learning methods such as MIRA (McDonald, Crammer, and Pereira 2005). The approach of deterministic classiﬁer-based parsing was ﬁrst proposed for Japanese by Kudo and Matsumoto (2002) and for English by Yamada and Matsumoto (2003). In contrast to spanning tree parsing, this can be characterized as a greedy inference strategy, trying to construct a globally optimal dependency graph by making a sequence of locally optimal decisions. The ﬁrst strictly incremental parser of this kind was described in Nivre (2003) and used for classiﬁer-based parsing of Swedish by Nivre, Hall, and Nilsson (2004) and English by Nivre and Scholz (2004). Altogether it has now been applied to 19 different languages (Nivre et al. 2006, 2007; Hall et al. 2007). Most algorithms in this tradition are restricted to projective dependency graphs, but it is 549 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Computational Linguistics Volume 34, Number 4 possible to recover non-projective dependencies using pseudo-projective parsing (Nivre and Nilsson 2005). More recently, algorithms for non-projective classiﬁer-based parsing have been proposed by Attardi (2006) and Nivre (2006a). The strictly deter- ministic parsing strategy has been relaxed in favor of n-best parsing by Johansson and Nugues (2006), among others. The dominant learning method in this tradition is support vector machines (Kudo and Matsumoto 2002; Yamada and Matsumoto 2003; Nivre et al. 2006) but memory-based learning has also been used (Nivre, Hall, and Nilsson 2004; Nivre and Scholz 2004; Attardi 2006). Of the algorithms described in this article, the arc-eager stack-based algorithm is essentially the algorithm proposed for unlabeled dependency parsing in Nivre (2003), extended to labeled dependency parsing in Nivre, Hall, and Nilsson (2004), and most fully described in Nivre (2006b). The major difference is that the parser is now initialized with the special root node on the stack, whereas earlier formulations had an empty stack at initialization.15 The arc-standard stack-based algorithm is brieﬂy described in Nivre (2004) but can also be seen as an incremental version of the algorithm of Yamada and Matsumoto (2003), where incrementality is achieved by only allowing one left-to- right pass over the input, whereas Yamada and Matsumoto perform several iterations in order to construct the dependency graph bottom-up, breadth-ﬁrst as it were. The list-based algorithms are both inspired by the work of Covington (2001), although the formulations are not equivalent. They have previously been explored for deterministic classiﬁer-based parsing in Nivre (2006a, 2007). A more orthodox implementation of Covington’s algorithms for data-driven dependency parsing is found in Marinov (2007). 8. Conclusion In this article, we have introduced a formal framework for deterministic incremental dependency parsing, where parsing algorithms can be deﬁned in terms of transition systems that are deterministic only together with an oracle for predicting the next transition. We have used this framework to analyze four different algorithms, proving the correctness of each algorithm relative to a relevant class of dependency graphs, and giving complexity results for each algorithm. To complement the formal analysis, we have performed an experimental evaluation of accuracy and efﬁciency, using SVM classiﬁers to approximate oracles, and using data from 13 languages. The comparison shows that although strictly projective dependency parsing is most efﬁcient both in learning and in parsing, the capacity to produce non- projective dependency graphs leads to better accuracy unless it can be assumed that all structures are strictly projective. The evaluation also shows that using the non- projective, list-based parsing algorithm gives a more stable improvement in this respect than applying the pseudo-projective parsing technique to a strictly projective parsing algorithm. Moreover, despite its quadratic time complexity, the non-projective parser is often as efﬁcient as the pseudo-projective parsers in practice, because the extended set of dependency labels used in pseudo-projective parsing slows down classiﬁcation. This demonstrates the importance of complementing the theoretical analysis of complexity with practical running time experiments. Although the non-projective, list-based algorithm can be said to give the best trade- off between accuracy and efﬁciency when results are averaged over all languages in the sample, we have also observed important language-speciﬁc effects. In particular, the 15 The current version was ﬁrst used in the CoNLL-X shared task (Nivre et al. 2006). 550 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / c o l i / l a r t i c e - p d f / / / / 3 4 4 5 1 3 1 8 0 8 9 4 5 / c o l i . 0 7 - 0 5 6 - r 1 - 0 7 - 0 2 7 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Nivre Deterministic Incremental Dependency Parsing arc-eager strategy inherent not only in the arc-eager, stack-based algorithm but also in both versions of the list-based algorithm appears to be suboptimal for some languages and syntactic representations. In such cases, using the arc-standard parsing strategy, with or without pseudo-projective parsing, may lead to signiﬁcantly higher accuracy. More research is needed to determine exactly which properties of linguistic structures and their syntactic analysis give rise to these effects. On the whole, however, the four algorithms investigated in this article give very similar performance both in terms of accuracy and efﬁciency, and several previous studies have shown that both the stack-based and the list-based algorithms can achieve state-of-the-art accuracy together with properly trained classiﬁers (Nivre et al. 2006; Nivre 2007; Hall et al. 2007). Acknowledgments I want to thank my students Johan Hall and Jens Nilsson for fruitful collaboration and for their contributions to the MaltParser system, which was used for all experiments. I also want to thank Sabine Buchholz, Matthias Buch-Kromann, Walter Daelemans, G ¨uls¸en Eryi ˘git, Jason Eisner, Jan Hajiˇc, Sandra K ¨ubler, Marco Kuhlmann, Yuji Matsumoto, Ryan McDonald, Kemal Oﬂazer, Kenji Sagae, Noah A. Smith, and Deniz Yuret for useful discussions on topics relevant to this article. I am grateful to three anonymous reviewers for many helpful suggestions that helped improve the ﬁnal version of the article. 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