A Comparative Study of Minimally
Supervised Morphological Segmentation

Teemu Ruokolainen∗
Aalto University

Oskar Kohonen∗∗
Aalto University

Kairit Sirts†
Tallinn University of Technology

Stig-Arne Grönroos∗
Aalto University

Mikko Kurimo∗
Aalto University

Sami Virpioja∗∗
Aalto University

This article presents a comparative study of a subfield of morphology learning referred to
as minimally supervised morphological segmentation. In morphological segmentation,
word forms are segmented into morphs, the surface forms of morphemes. In the minimally
supervised data-driven learning setting, segmentation models are learned from a small number
of manually annotated word forms and a large set of unannotated word forms. In addition to
providing a literature survey on published methods, we present an in-depth empirical comparison
on three diverse model families, including a detailed error analysis. Based on the literature
survey, we conclude that the existing methodology contains substantial work on generative
morph lexicon-based approaches and methods based on discriminative boundary detection. As for
which approach has been more successful, both the previous work and the empirical evaluation
presented here strongly imply that the current state of the art is yielded by the discriminative
boundary detection methodology.

∗ Department of Signal Processing and Acoustics, Otakaari 5 A, FI-02150 Espoo, Finland.

Email: {teemu.ruokolainen, stig-arne.gronroos, mikko.kurimo}@aalto.fi.

∗∗ Department of Information and Computer Science, Konemiehentie 2, FI-02150 Espoo, Finland.

Email: {oskar.kohonen, sami.virpioja}@aalto.fi.

† Institute of Cybernetics, Akadeemia tee 21 EE-12618 Tallin, Estonia. Email: sirts@phon.ioc.ee.

Einreichung erhalten: 15 September 2014; revised version received: 7 Oktober 2015; zur Veröffentlichung angenommen:
15 November 2015

doi:10.1162/COLI_a_00243

© 2016 Verein für Computerlinguistik

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Computerlinguistik

Volumen 42, Nummer 1

1. Einführung

This article discusses a subfield of morphology learning referred to as morphological
segmentation, in which word forms are segmented into morphs, the surface forms
of morphemes. Zum Beispiel, consider the English word houses with a corresponding
segmentation house+s, where the segment house corresponds to the word stem and the
suffix -s marks the plural number. Although this is a major simplification of the diverse
morphological phenomena present in languages, this type of analysis has nevertheless
been of substantial interest to computational linguistics, beginning with the pioneering
work on morphological learning by Harris (1955). As for automatic language process-
ing, such segmentations have been found useful in a wide range of applications, In-
cluding speech recognition (Hirsimäki et al. 2006; Narasimhan et al. 2014), Information
retrieval (Turunen and Kurimo 2011), maschinelle Übersetzung (de Gispert et al. 2009; Grün
and DeNero 2012), and word representation learning (Luong, Socher, and Manning
2013; Qiu et al. 2014).

Since the early work of Harris (1955), most research on morphological segmentation
has focused on unsupervised learning, which aims to learn the segmentation from a
list of unannotated (unlabeled) word forms. The unsupervised methods are appealing
as they can be applied to any language for which there exists a sufficiently large set
of unannotated words in electronic form. Folglich, such methods provide an inex-
pensive means of acquiring a type of morphological analysis for low-resource languages
as motivated, Zum Beispiel, by Creutz and Lagus (2002). The unsupervised approach and
learning setting has received further popularity because of its close relationship with the
unsupervised word segmentation problem, which has been viewed as a realistic setting
for theoretical study of language acquisition (Brent 1999; Goldwater 2006).

Although development of novel unsupervised model formulations has remained
a topic of active research (Poon, Cherry, and Toutanova 2009; Monson, Hollingshead,
and Roark 2010; Spiegler and Flach 2010; Lee, Haghighi, and Barzilay 2011; Sirts
and Goldwater 2013), recent work has also shown a growing interest towards semi-
supervised learning (Poon, Cherry, and Toutanova 2009; Kohonen, Virpioja, and Lagus
2010; Sirts and Goldwater 2013; Grönroos et al. 2014; Ruokolainen et al. 2014). Allgemein,
the aim of semi-supervised learning is to acquire high-performing models utilizing both
unannotated as well as annotated data (Zhu and Goldberg 2009). In morphological seg-
mentation, the annotated data sets are commonly small, on the order of a few hundred
word forms. We refer to this learning setting with such a small amount of supervision as
minimally supervised learning. In consequence, similar to the unsupervised methods,
the minimally supervised techniques can be seen as a means of acquiring a type of
morphological analysis for under-resourced languages.

Individual articles describing novel methods typically contain a comparative dis-
cussion and empirical evaluation between one or two preceding approaches. daher,
what is currently lacking from the literature is a summarizing comparative study on the
published methodology as a whole. Darüber hinaus, the literature currently lacks discussion
on error analysis. A study on the error patterns produced by varying approaches could
inform us about their potential utility in different tasks. Zum Beispiel, if an application
requires high-accuracy compound splitting, one could choose to apply a model with a
good compound-splitting capability even if its affix accuracy does not reach state of the
Kunst. The purpose of this work is to address these issues.

Our main contributions are as follows. Erste, we present a literature survey on
morphological segmentation methods applicable in the minimally supervised learning
setting. The considered methods include unsupervised techniques that learn solely from

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Minimally Supervised Morphological Segmentation

unannotated data, supervised methods that utilize solely annotated data, and semi-
supervised approaches that utilize both unannotated and annotated data. Zweite, Wir
perform an extensive empirical evaluation of three diverse method families, including a
detailed error analysis. The approaches considered in this comparison are variants of the
Morfessor algorithm (Creutz and Lagus 2002, 2005, 2007; Kohonen, Virpioja, and Lagus
2010; Grönroos et al. 2014), the adaptor grammar framework (Sirts and Goldwater 2013),
and the conditional random field method (Ruokolainen et al. 2013, 2014). We hope the
presented discussion and empirical evaluation will be of help for future research on the
considered task.

The rest of the article is organized as follows. In Section 2, we provide an overview
of related studies. We then provide a literature survey of published morphological
segmentation methodology in Section 3. Experimental work is presented in Section 4.
Endlich, we provide a discussion on potential directions for future work and conclusions
on the current work in Sections 5 Und 6, jeweils.

2. Related Work

Hammarström and Borin (2011) presented a literature survey on unsupervised learning
of morphology, including methods for learning morphological segmentation. Whereas
the discussion provided by Hammarström and Borin focuses mainly on linguistic
aspects of morphology learning, our work is strongly rooted in machine learning
methodology and empirical evaluation. Zusätzlich, whereas Hammarström and Borin
focus entirely on unsupervised learning, our work considers a broader range of learn-
ing paradigms. daher, although related, Hammarström and Borin and our current
presentation are complementary in that they have different focus areas.

In addition to the work of Hammarström and Borin (2011), we note that there exists
some established forums on morphology learning. Erste, we mention the ACL Special
Interest Group on Computational Morphology and Phonology (SIGMORPHON), welche
has regularly organized workshops on the subject since 2002. As for specifically mor-
phology learning, we refer to the Morpho Challenge competitions organized since 2005
at Aalto University (formerly known as Helsinki University of Technology). Obwohl
these events have been successful in providing a publication and discussion venue for
researchers interested in the topic, they have not given birth to comparative studies
or survey literature. Zum Beispiel, whereas the publications on the Morpho Challenge
(Kurimo et al. 2009; Kurimo, Virpioja, and Turunen 2010) discuss the competition
results, they nevertheless do not attempt to provide any insight on the fundamental
differences and similarities of the participating methods.

3. Methoden

This section provides a detailed review of our methodology. We begin by describing
varying morphological representations, including segmentation, and the minimally
supervised learning setting in Sections 3.1 Und 3.2, jeweils. We then provide a
literature survey and comparative discussion on a range of methods in Section 3.3.

3.1 On Learning Morphological Representations

In what follows, we briefly characterize morphological segmentation with respect to
alternative morphological representations, particularly the full morphological anal-
ysis. Zu diesem Zweck, consider the exemplar segmentations and full analyses for Finnish

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Computerlinguistik

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Tisch 1
Morphological segmentation versus full morphological analysis for exemplar Finnish word
Formen. The full analysis consists of word lemma (basic form), part-of-speech, and fine-grained
labels.

word form

full analysis

segmentation

auto (car)
autossa (in car)
autoilta (from cars)
autoilta (car evening)
maantie (highway)

sähköauto (electric car)

auto+N+Sg+Nom
auto+N+Sg+Ine
auto+N+Pl+Abl
auto+N+Sg+Nom+# ilta+N+Sg+Nom
maantie+N+Sg+Nom
maa+N+Sg+Gen+# tie+N+Sg+Nom
sähköauto+N+Sg+Nom
sähkö+N+Sg+Nom+# auto+N+Sg+Nom sähkö+auto

auto
auto+ssa
auto+i+lta
auto+ilta
maantie
maa+n+tie
sähköauto

word forms in Table 1, where the full analyses are provided by the rule-based OMorFi
analyzer developed by Pirinen (2008). Note that it is typical for word forms to have
alternative analyses and/or meanings that cannot be disambiguated without sentential
Kontext. Evidently, the level of detail in the full analysis is substantially higher compared
with the segmentation, as it contains lemmatization as well as morphological tagging,
whereas the segmentation consists of only segment boundary positions. Folglich,
because of this simplification, morphological segmentation has been amenable to un-
supervised machine learning methodology, beginning with the work of Harris (1955).
In der Zwischenzeit, the majority of work on learning of full morphological analysis has used
supervised methodology (Chrupala, Dinu, and van Genabith 2008). zuletzt, there have
been numerous studies on statistical learning of intermediate forms of segmentation
and full analysis (Lignos 2010; Virpioja, Kohonen, and Lagus 2010) as well as alternative
morphological representations (Yarowsky and Wicentowski 2000; Schone and Jurafsky
2001; Neuvel and Fulop 2002; Johnson and Martin 2003).

As for language processing, learning segmentation can be advantageous com-
pared with learning full analyses. Insbesondere, learning full analysis in a supervised
manner typically requires up to tens of thousands of manually annotated sentences.
A low-cost alternative, daher, could be to learn morphological segmentation from
unannotated word lists and a handful of annotated examples. Wichtig, segmen-
tation analysis has been found useful in a range of applications, such as speech
recognition (Hirsimäki et al. 2006; Narasimhan et al. 2014), information retrieval
(Turunen and Kurimo 2011), maschinelle Übersetzung (de Gispert et al. 2009; Green and
DeNero 2012), and word representation learning (Luong, Socher, and Manning 2013;
Qiu et al. 2014).

Despite its intuitiveness, it should be noted that the segmentation representation
is not equally applicable to all languages. Zu diesem Zweck, consider the terms isolative
and synthetic languages. In languages with a high amount of isolating morpholog-
ical properties, word forms tend to comprise their own morphemes. In der Zwischenzeit, In
heavily synthetic languages, words tend to contain multiple morphemes. Synthetic
languages can be described further according to their agglutinative (concatenative) Und
fusional properties. In the former, the morphs tend to have clear boundaries between
them whereas in the latter, the morphs tend to be indistinguishable. For examples
of agglutinative and fusional word formation, consider the English verbs played (Vergangenheit
tense of play) and sang (past tense of sing). Where the previous can be effortlessly

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Minimally Supervised Morphological Segmentation

divided into two segments as play+ed (STEM + PAST TENSE), there are no such distinct
boundaries in the latter. Generally, languages with synthetic properties mix concate-
native and fusional schemes and contain agglutinative properties to varying degrees.
Morphological segmentation can be most naturally applied to highly agglutinative
languages.

Morphologically ambiguous word forms are common especially in highly synthetic
languages. Even without disambiguation based on sentential context, providing all
correct alternatives could be useful for some downstream applications, such as informa-
tion retrieval. Statistical methods can usually provide n-best segmentations; for exam-
Bitte, Morfessor (Creutz and Lagus 2007) and CRFs (Ruokolainen et al. 2013) by using
n-best Viterbi algorithm and adaptor grammar (Sirts and Goldwater 2013) by collecting
the variations in the posterior distribution samples. Although there is no evident way
to decide the correct number of alternatives for a particular word form, n-best lists
might be useful whenever recall (including the correct answers) is more important than
precision (excluding any incorrect answers). The Morpho Challenge competitions have
allowed providing alternative segmentations for the submitted methods, but no clear
developments have been reported. Tatsächlich, even in the reference results based on the gold
standard segmentations, selecting all alternative segmentations has performed slightly
worse in the information retrieval tasks than taking only the first segmentation (Kurimo,
Virpioja, and Turunen 2010).

3.2 Minimally Supervised Learning Settings

In data-driven morphological segmentation, our aim is to learn segmentation models
from training data. Subsequent to training, the models provide segmentations for given
word forms. In the minimally supervised learning setting, as defined here, the models
are estimated from annotated and unannotated word forms. We denote the annotated
data set comprising word forms with their corresponding segmentation as D and the
unannotated data set comprising raw word forms as U. Typically, the raw word forms
can be obtained easily and, consequently, U can contain millions of word forms. Mean-
while, acquiring the annotated data D requires manual labor and, daher, typically
contains merely hundreds or thousands of word forms. For an illustration of D and U,
siehe Tabelle 2.

Tisch 2
Examples of annotated and unannotated data, D and U, jeweils. Typically, U can contain
hundreds of thousands or millions of word forms, whereas D contains merely hundreds or
thousands of word forms.

D

U

anarch + ist + S
bound + Hrsg
conting + ency
von + fame
entitle + ment
fresh + man
. . .

Aktionen
bilinguals
Gemeinschaft
disorders
equipped
faster
. . .

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We consider three machine learning approaches applicable in the minimally su-
pervised learning setting, nämlich, unsupervised, supervised, and semi-supervised
learning. In unsupervised learning, the segmentation models are trained on solely
unannotated data U. In der Zwischenzeit, supervised models are trained from solely the anno-
tated data D. Endlich, the aim of semi-supervised learning is to utilize both the available
unannotated and annotated data. Because the semi-supervised approach utilizes the
largest amount of data, it is expected to be most suitable for acquiring high segmenta-
tion accuracy in the minimally supervised learning setting.

zuletzt, we note that the unsupervised learning framework can be understood in a
strict or non-strict sense, depending on whether the applied methods are allowed to use
annotated data D for hyperparameter tuning. Although the term unsupervised learning
itself suggests that such adjusting is infeasible, this type of tuning is nevertheless
common (Creutz et al. 2007; Çöltekin 2010; Spiegler and Flach 2010; Sirts and Goldwater
2013). Zusätzlich, the minimally supervised learning setting explicitly assumes a small
amount of available annotated word forms. Folglich, in the remainder of this
Artikel, all discussion on unsupervised methods refers to unsupervised learning in the
non-strict sense.

3.3 Algorithms

Here we provide a literature survey on proposed morphological segmentation methods
applicable in the minimally supervised learning setting. We place particular emphasis
on three method families, nämlich, the Morfessor algorithm (Creutz and Lagus 2002,
2005, 2007; Kohonen, Virpioja, and Lagus 2010; Grönroos et al. 2014), the adaptor
grammar framework (Sirts and Goldwater 2013), and conditional random fields
(Ruokolainen et al. 2013, 2014). These approaches are the subject of the empirical evalu-
ation presented in Section 4. We present individual method descriptions in Section 3.3.1.
Subsequently, Abschnitt 3.3.2 provides a summarizing discussion, the purpose of which
is to gain insight on the fundamental differences and similarities between the varying
approaches.

3.3.1 Descriptions

Morfessor. We begin by describing the original, unsupervised Morfessor method fam-
ily (Creutz and Lagus 2002, 2005, 2007). We then discuss the later, semi-supervised
extensions (Kohonen, Virpioja, and Lagus 2010; Grönroos et al. 2014). Insbesondere,
we review the extension of Morfessor Baseline to semi-supervised learning by using
a weighted generative model (Kohonen, Virpioja, and Lagus 2010), and then discuss
the most recent Morfessor variant, FlatCat (Grönroos et al. 2014). Endlich, we discuss
some general results from the literature on semi-supervised learning with generative
Modelle.

The unsupervised Morfessor methods are based on a generative probabilistic
model that generates the observed word forms xi ∈ U by concatenating morphs xi =
mi1 ◦ mi2 ◦ · · · ◦ min. The morphs are stored in a morph lexicon, which defines the
probability of each morph P(M | θ) given some parameters θ. The Morfessor learning
problem is to find a morph lexicon that strikes an optimal balance between encoding the
observed word forms concisely and, gleichzeitig, having a concise morph lexicon.
Zu diesem Zweck, Morfessor utilizes a prior distribution P(θ) over morph lexicons, derived
from the Minimum Description Length principle (Rissanen 1989), that favors lexicons
that contain fewer, shorter morphs. This leads to the following minimization problem

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that seeks to balance the conciseness of the lexicon with the conciseness of the observed
corpus encoded with the lexicon:

θ∗ = arg min

L(θ, U ) = arg min

{− ln P(θ) − ln P(U | θ)} ,

(1)

θ

θ

The optimization problem in Equation (1) is complicated by the fact that each word
in the corpus U can be generated by different combinations of morphs, defined by
the set of segmentations of that word. This introduces a nuisance parameter z for the
segmentation of each word form, where P(U | θ) = (cid:80)
Z P(U | z, θ)P(z). Because of this
summation, the expression cannot be solved analytically, and iterative optimization
must be used instead.

The unsupervised Morfessor variants differ in the following ways: Zuerst, ob
all morphs belong to a single category or the categories PREFIX, STEM, and SUFFIX are
gebraucht; secondly, if the model utilizes a lexicon that is flat or hierarchical. In a flat lexicon,
morphs can only be encoded by combining letters, whereas in a hierarchical lexicon
pre-existing morphs can be used for storing longer morphs. Thirdly, the parameter
estimation and inference methods differ. Parameters are estimated using greedy local
search or iterative batch procedures while inference is performed with either Viterbi
decoding or heuristic procedures.

The earliest Morfessor method, referred to as Morfessor Baseline, has been extended
to semi-supervised learning by Kohonen, Virpioja, and Lagus (2010). Im Gegensatz, Die
later methods, nämlich, Categories-ML and Categories-MAP, have not been extended,
as they use either hierarchical lexicons or training procedures that make them less
amenable to semi-supervised learning. Jedoch, recently Grönroos et al. (2014) Profi-
posed a new Morfessor variant that uses morph categories in combination with a flat
lexicon, and can therefore apply the semi-supervised learning technique of Kohonen,
Virpioja, and Lagus.

We begin the description of the semi-supervised extension to Morfessor Baseline
(Creutz and Lagus 2002, 2007) by reviewing its generative model. Morfessor Baseline
utilizes a model in which word forms are generated by concatenating morphs, alle
of which belong to the same category. It utilizes a flat morph lexicon P(M | θ) Das
is simply a multinomial distribution over morphs m, according to the probabilities
given by the parameter vector θ. The utilized prior penalizes storing long morphs
in the lexicon by assigning each stored morph a cost that depends most strongly on
the morph length in letters. A morph is considered to be stored if the lexicon as-
signs it a nonzero probability. The parameter estimation for θ finds a local optimum
utilizing greedy local search. The search procedure approximates the optimization
problem in Equation (1) by assuming that, for each word form xi, its corresponding
segmentation distribution P(zi) has all its mass concentrated to a single segmentation
zi. The parameter estimation is then performed by locally searching each word for
the segmentation that yields the best value of the cost function in Equation (1). Der
process is repeated for all words in random order until convergence. Subsequent to
learning, the method predicts the segmentation of a word form by selecting the seg-
mentation with the most probable sequence of morphs using an extension of the Viterbi
Algorithmus.

Semi-supervised learning is in principle trivial for a generative model: For the la-
beled word forms D, the segmentation is fixed to its correct value, and for the unlabeled
forms U the standard parameter estimation procedure is applied. Jedoch, Kohonen,

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Virpioja, and Lagus (2010) failed to achieve notable improvements in this fashion, Und
consequently replaced the minimized function L in Equation (1) mit

L(θ, z, U, D) = − ln P(θ) − α × ln P(U | θ) − β × ln P(D | θ).

(2)

Such weighted objectives were used earlier in combination with generative models by,
Zum Beispiel, Nigam et al. (2000). The semi-supervised training procedure then adjusts
the weight values α and β. The absolute values of the weights control the cost of
encoding a morph in the training data with respect to the cost of adding a new morph to
the lexicon, and their ratio controls how much weight is placed on the annotated data
with respect to the unannotated data. When the hyperparameters α and β are fixed,
the lexicon parameters θ can be optimized with the same greedy local search procedure
as in the unsupervised Morfessor Baseline. The weights can then be optimized with a
grid search and by choosing the model with the best evaluation score on a held-out
development set. Although this modification is difficult to justify from the perspective
of generative modeling, Kohonen, Virpioja, and Lagus show that in practice it can
yield performance improvements. From a theoretical point of view, it can be seen
as incorporating discriminative training techniques when working with a generative
model by optimizing for segmentation performance rather than maximum a posteriori
probability. Jedoch, only the hyperparameters are optimized in this fashion, wohingegen
the lexicon parameters are still learned within the generative model framework.

The semi-supervised learning strategy described here is simple to apply if the
objective function in Equation (1) can be factored to parts that encode the morphs
using letters and encode the training corpus using the morphs. For some models of the
Morfessor family this is not possible because of the use of a hierarchical lexicon, Wo
morphs can be generated from other morphs as well as from individual letters. Im Par-
besonders, this includes the well-performing Categories-MAP variant (Creutz et al. 2007).
In contrast to Morfessor Baseline, the Categories-MAP and the preceding Categories-
ML method use a hidden Markov model to produce the observed words, bei dem die
states are given by STEM, PREFIX, SUFFIX categories as well as an internal non-morpheme
category. A recent development is Morfessor FlatCat by Grönroos et al. (2014), welche
uses the hidden Markov model structure and morph categories in combination with a
flat lexicon, thus allowing semi-supervised learning in the same fashion as for Morfessor
Baseline.

Allgemein, the key idea behind using the weighted objective function in Equation
(2) for semi-supervised learning is that the hyperparameters α and β can be used to
explicitly control the influence of the unannotated data on the learning. Similar semi-
supervised learning strategies have also been used in other problems. For classification
with generative models, it is known that adding unlabeled data to a model trained with
labeled data can degrade performance (Cozman et al. 2003; Cozman and Cohen 2006).
Insbesondere, this can be the case if the generative model does not match the generating
Verfahren, something that is difficult to ensure in practice. Kürzlich, this phenomenon was
analyzed in more detail by Fox-Roberts and Rosten (2014), who show that, although the
unlabeled data can introduce a bias, the bias can be removed by optimizing a weighted
likelihood function where the unlabeled data is raised to the power NL
N , where NL is the
number of labeled samples and N is the number of all samples. This corresponds to the
weighting scheme used in Morfessor when setting the ratio α

β = NL
N .

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Minimally Supervised Morphological Segmentation

Adaptor Grammars. Kürzlich, Sirts and Goldwater (2013) presented work on minimally
supervised morphological segmentation using the adaptor grammar (AG) Ansatz
(Johnson, Griffiths, and Goldwater 2006). The AGs are a non-parametric Bayesian mod-
eling framework applicable for learning latent tree structures over an input corpus of
strings. They can be used to define morphological grammars of different complexity,
starting from the simplest grammar where each word is just a sequence of morphs and
extending to more complex grammars, where each word consists, Zum Beispiel, of zero
or more prefixes, a stem, and zero or more suffixes.

The actual forms of the morphs are learned from the data and, subsequent to
learning, used to generate segmentations for new word forms. In this general approach,
AGs are similar to the Morfessor family (Creutz and Lagus 2007). A major difference,
Jedoch, is that the morphological grammar is not hard-coded but instead specified as
an input to the algorithm. This allows different grammars to be explored in a flexible
manner. Prior to the work by Sirts and Goldwater, the AGs were successfully applied
in a related task of segmenting utterances into words (Johnson 2008; Johnson and
Goldwater 2009; Johnson and Demuth 2010).

The second major difference between the Morfessor family and the AG framework
is the contrast between the MAP and fully Bayesian estimation approaches. Whereas
the search procedure of the Morfessor method discussed earlier returns a single model
corresponding to the MAP point-estimate, AGs instead operate with full posterior
distributions over all possible models. Because acquiring the posteriors analytically
is intractable, inference is performed utilizing Markov chain Monte Carlo algorithms
to obtain samples from the posterior distributions of interest (Johnson 2008; Johnson
and Goldwater 2009; Johnson and Demuth 2010; Sirts and Goldwater 2013). Jedoch,
as sampling-based models are costly to train on large amounts of data, we adopt the
parsing-based method proposed in Sirts and Goldwater (2013) to use the trained AG
model inductively on test data. One of the byproducts of training the AG model is the
posterior grammar, which in addition to all the initial grammar rules, also contains
the cached subtrees learned by the system. This grammar can be used in any standard
parser to obtain segmentations for new data.

The AG framework was originally designed for the unsupervised learning setting,
but Sirts and Goldwater (2013) introduced two approaches for semi-supervised learn-
ing they call the semi-supervised AG and AG Select methods. The semi-supervised
AG approach is an extension to unsupervised AG, in which the annotated data D is
exploited in a straightforward manner by keeping the annotated parts of parse trees
fixed while inferring latent structures for the unannotated parts. For unannotated word
Formen, inference is performed on full trees. Zum Beispiel, the grammar may specify that
words are sequences of morphs and each morph is a sequence of submorphs. Typically,
the annotated data only contain morpheme boundaries and submorphs are latent in this
Kontext. In this situation the inference for annotated data is performed over submorph
structures only.

Similarly to unsupervised learning, semi-supervised AG requires the morpholog-
ical grammar to be defined manually. In der Zwischenzeit, the AG Select approach aims to
automate the grammar development process by systematically evaluating a range of
grammars and finding the best one. AG Select is trained using unsupervised AG with
an uninformative metagrammar so that the resulting parse-trees contain many possible
segmentation templates. To find out which template works the best for any given
language or data set, each of these templates are evaluated using the annotated data
set D. In this sense, AG Select can be characterized as more of a model selection method
than semi-supervised learning.

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Conditional Random Fields. The Morfessor and AG algorithms discussed earlier, although
different in several respects, operate in a similar manner in that they both learn lexicons.
For Morfessor, the lexicon consists of morphs, whereas for AG, the lexical units are
partial parse-trees. Subsequent to learning, new word forms are segmented either by
generating the most likely morph sequences (Morfessor) or by sampling parse trees
from the posterior distribution (AG). In what follows, we consider a different approach
to segmentation using sequence labeling methodology. The key idea in this approach
is to focus the modeling effort to morph boundaries instead of the whole segments.
Following the presentation of Ruokolainen et al. (2013, 2014), the morphological seg-
mentation task can be represented as a sequence labeling problem by assigning each
character in a word form to one of three classes, nämlich,

beginning of a multi-character morph

B
M middle of a multi-character morph
S

single-character morph

Using this label set, one can represent the segmentation of the Finnish word autoilta
(from cars) (auto+i+lta) als

u
↓

A
A
↓
↓
B M M M S B M M

Ö
↓

ich
↓

T
↓

T
↓

l
↓

Naturally, one can also use other label sets. Im Wesentlichen, by defining more fine-grained
labels, one captures increasingly eloquent structure but begins to overfit model to the
training data because of increasingly sparser statistics. Subsequent to defining the label
set, one can learn a segmentation model using general sequence labeling methods, solch
as the well-known conditional random field (CRF) Rahmen (Lafferty, McCallum, Und
Pereira 2001).

Denoting the word form and the corresponding label sequence as x and y, bzw-
aktiv, the CRFs directly model the conditional probability of the segmentation given the
word form, das ist, P(j | X; w). The model parameters w are estimated discriminatively
from the annotated data set D using iterative learning algorithms (Lafferty, McCallum,
and Pereira 2001; Collins 2002). Subsequent to estimation, the CRF model segments
word forms x by using maximum a posteriori (MAP) graph inference, das ist, solving
an optimization problem

z = arg max

P (u | X; w)

u

(3)

using the standard Viterbi search (Lafferty, McCallum, and Pereira 2001).

As it turns out, the CRF model can learn to segment words with a surprisingly high
accuracy from a relatively small D, das ist, without utilizing any of the available unanno-
tated word forms U. Insbesondere, Ruokolainen et al. (2013) showed that it is sufficient to
use simple left and right substring context features that are naturally accommodated by
the discriminative parameter estimation procedure. Darüber hinaus, Ruokolainen et al. (2014)
showed that the CRF-based approach can be successfully extended to semi-supervised
learning settings in a straightforward manner via feature set expansion by utilizing
predictions of unsupervised segmentation algorithms. By utilizing this approach, Die
CRF model learns to associate the output of the unsupervised algorithms, such as the
Morfessor and adaptor grammar methods, in relation to the surrounding substring
Kontext.

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Minimally Supervised Morphological Segmentation

Other Work. In addition to the algorithms discussed here, there exist numerous other
segmentation approaches applicable in the minimally supervised learning setting. Als
the earliest example of work in this line, consider obtaining segmentations using the
classic letter successor variety (LSV) method of Harris (1955). The LSV method utilizes
the insight that the predictability of successive letters should be high within morph
segments, and low at the boundaries. Folglich, a high variety of letters following
a prefix indicates a high probability of a boundary. Whereas LSV score tracks pre-
dictability given prefixes, the same idea can be utilized for suffixes, providing the letter
predecessor variety (LPV) method. As for the minimally supervised learning setting, Die
LSV/LPV method can be used most straightforwardly by counting the LSV/LPV scores
from unannotated data and, subsequently, tuning the necessary threshold values on the
annotated data (Çöltekin 2010). Andererseits, one could also use the LSV/LPV
values as features for a classification model, in which case the threshold values can be
learned discriminatively based on the available annotated data. The latter approach is
essentially realized in the event the LSV/PSV scores are provided for the CRF model
discussed earlier (Ruokolainen et al. 2014).

As for more recent work, we first refer to the generative log-linear model of Poon,
Cherry, and Toutanova (2009). Similarly to the Morfessor model family, this approach
is based on defining a joint probability distribution over the unannotated word forms
U and the corresponding segmentations S. The distribution is log-linear in form and
is denoted as p(U, S; θ), where θ is the model parameter vector. Wieder, similarly to
the Morfessor framework, Poon, Cherry, and Toutanova (2009) learn a morph lexicon
that is subsequently used to generate segmentations for new word forms. The learning
is controlled using prior distributions on both corpus and lexicon, which penalize ex-
ceedingly complex morph lexicon (similarly to Morfessor) and exceedingly segmented
corpus, jeweils. The log-linear form of p(U, S; θ) enables the approach to use a wide
range of overlapping features. Insbesondere, Poon, Cherry, and Toutanova (2009) utilize a
morph-context feature set with individual features defined for each morph and morph
substring contexts. In addition to unsupervised learning, they present experiments in
the semi-supervised setting. Konkret, they accomplish this by fixing the segmen-
tations of annotated words in D, according to their gold standard segmentation. Notiz,
Jedoch, that this approach of extending a generative model does not necessarily utilize
the supervision efficiently, as discussed previously regarding the Morfessor method
family.

Endlich, we briefly mention a range of recently published methods (Monson,
Hollingshead, and Roark 2010; Spiegler and Flach 2010; Kılıç and Boz¸sahin 2012; Eger
2013). The Paramor approach presented by Monson, Hollingshead, and Roark (2010)
defines a rule-based system for unsupervised learning of morphological paradigms.
The Promodes system of Spiegler and Flach (2010) defines a family of generative prob-
abilistic models for recovering segment boundaries in an unsupervised fashion. Der
algorithm of Kılıç and Boz¸sahin (2012) is based on a generative hidden Markov model
(HMM), in which the HMM learns to generate morph sequences for given word forms
in a semi-supervised fashion. Endlich, Eger (2013) presents work on fully supervised
segmentation by exhaustive enumeration and a generative Markov model on morphs.
As for the minimally supervised learning setting, the Paramor system learns mainly
from unannotated data U and utilizes annotated data D to adjust the required threshold
value. The Promodes models can be trained either in an unsupervised manner on U or
in a supervised manner on D. The algorithm of Kılıç and Boz¸sahin (2012) learns mainly
from unannotated data U and incorporates supervision from the annotated corpus in
the form of manually selected statistics: the inclusion of the statistics yields a large

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improvement in performance. zuletzt, in their work with the supervised enumeration
Ansatz, Eger (2013) assumes a large (on the order of tens of thousands) amount of
annotated word forms available for learning. Daher, it is left for future work to determine
if the approach could be applied successfully in the minimally supervised learning
setting.

3.3.2 Summary. Here we aim to summarize the fundamental differences and similarities
between the varying learning approaches discussed in the previous section.

Learning Lexicons versus Detecting Boundaries. We begin by dividing the methods
described earlier into two—lexicon-based (Creutz et al. 2007; Poon, Cherry, Und
Toutanova 2009; Monson, Hollingshead, and Roark 2010; Kılıç and Boz¸sahin 2012;
Eger 2013; Sirts and Goldwater 2013) and boundary detection (Harris 1955; Spiegler
and Flach 2010; Ruokolainen et al. 2013)—categories. In the former, the model learns
lexical units, whereas in the latter the model learns properties of morph boundaries.
Zum Beispiel, in the case of Morfessor (Creutz et al. 2007) the lexical units correspond
to morphs whereas in AGs (Sirts and Goldwater 2013) the units are parse trees. Mean-
while, consider the CRF approach of Ruokolainen et al. (2013) and the classical approach
of Harris (1955), which identify morph boundary positions using substring contexts and
letter successor varieties, jeweils. Allgemein, whether it is easier to discover morphs
or morph boundaries is largely an empirical question. So far, only the method of Poon,
Cherry, and Toutanova (2009) has explicitly modeled both a morph lexicon and features
describing character n-grams at morpheme boundaries.

Generative versus Discriminative Learning. The second main distinction divides the
models into generative and discriminative approaches. The generative approaches
(Creutz et al. 2007; Poon, Cherry, and Toutanova 2009; Spiegler and Flach 2010; Monson,
Hollingshead, and Roark 2010; Kılıç and Boz¸sahin 2012; Eger 2013; Sirts and Goldwater
2013) model the joint distribution of word forms and their corresponding segmenta-
tionen, whereas discriminative (Harris 1955; Ruokolainen et al. 2013) approaches directly
estimate a conditional distribution of segmentation given a word form. Mit anderen Worten,
whereas generative methods generate both word forms and segmentations, the dis-
criminative methods generate only segmentations given word forms. The generative
models are naturally applicable for unsupervised learning. In der Zwischenzeit, discriminative
modeling always requires some annotated data, thus excluding the possibility of un-
supervised learning. zuletzt, it appears that most lexicon-based methods are generative
and most boundary detection methods are discriminative. Jedoch, note that this is a
trend rather than a rule, as exemplified by generative boundary detection method of
Spiegler and Flach (2010).

Semi-Supervised Learning Approaches. Both generative and discriminative models can be
extended to utilize annotated as well as unannotated data in a semi-supervised man-
ner. Jedoch, the applicable techniques differ. For generative models, semi-supervised
learning is in principle trivial: For the labeled word forms D, the segmentation is fixed
to its correct value, as exemplified by the approaches of Poon, Cherry, and Toutanova
(2009), Spiegler and Flach (2010), and Sirts and Goldwater (2013). Andererseits,
the semi-supervised setting also makes it possible to apply discriminative techniques
to generative models. Insbesondere, model hyperparameters can be selected to optimize
segmentation performance, rather than some generative objective, such as likelihood.
Special cases of hyperparameter selection include the weighted objective function

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(Kohonen, Virpioja, and Lagus 2010), data selection (Virpioja, Kohonen, and Lagus 2011;
Sirts and Goldwater 2013), and grammar template selection (Sirts and Goldwater 2013).
As for the weighted objective function and grammar template selection, the weights
and templates are optimized to maximize segmentation accuracy. In der Zwischenzeit, Daten
selection is based on the observation that omitting some of the training data can improve
segmentation accuracy (Virpioja, Kohonen, and Lagus 2011; Sirts and Goldwater 2013).
For discriminative models, the possibly most straightforward semi-supervised
learning technique is adding features derived from the unlabeled data, as exem-
plified by the CRF approach of Ruokolainen et al. (2014). Jedoch, discriminative,
semi-supervised learning is in general a much researched field with numerous diverse
Techniken (Zhu and Goldberg 2009). Zum Beispiel, merely for the CRF model alone,
there exist several proposed semi-supervised learning approaches (Jiao et al. 2006;
Mann and McCallum 2008; Wang et al. 2009).

On Local Search. In what follows, we will discuss a potential pitfall of some algorithms
that utilize local search procedures in the parameter estimation process, as exemplified
by the Morfessor model family (Creutz et al. 2007). As discussed in Section 3.3.1, Die
Morfessor algorithm finds a local optimum of the objective function using a local search
procedure. This complicates model development because if two model variants perform
differently empirically, it is uncertain whether it is because of a truly better model or
merely better fit with the utilized parameter estimation method, as discussed also by
Goldwater (2006, Abschnitt 4.2.2.3). daher, in contrast, within the adaptor grammar
Rahmen (Johnson, Griffiths, and Goldwater 2006; Sirts and Goldwater 2013), Die
focus has not been on finding a single best model, but rather on finding the posterior
distribution over segmentations of the words. Another approach to the problem of bad
local optima is to start a local search near some known good solution. This approach
is taken in Morfessor FlatCat, for which it was found that initializing the model with
the segmentations produced by the supervised CRF model (with a convex objective
Funktion) yields improved results (Grönroos et al. 2014).

4. Experimente

In diesem Abschnitt, we perform an empirical comparison of segmentation algorithms in
the semi-supervised learning setting. The purpose of the presented experiments is to
extend the current literature by considering a wider range of languages compared with
previous work, and by providing an in-depth error analysis.

4.1 Data

We perform the experiments on four languages, nämlich, English, Estonian, Finnish,
and Turkish. The English, Finnish, and Turkish data are from the Morpho Challenge
2009/2010 data set (Kurimo et al. 2009; Kurimo, Virpioja, and Turunen 2010). Der
annotated Estonian data set is acquired from a manually annotated, morphologi-
cally disambiguated corpus,1 and the unannotated word forms are gathered from the
Estonian Reference Corpus (Kaalep et al. 2010). Tisch 3 shows the total number of
instances available for model estimation and testing.

1 Available at http://www.cl.ut.ee/korpused/morfkorpus/index.php?lang=en.

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Tisch 3
Number of word types in the data sets.

English

Estonian

Finnish

Turkish

train (unannotated)
train (annotated)
Entwicklung
test

384,903
1,000
694
10×1,000

3,908,820
1,000
800
10×1,000

2,206,719
1,000
835

617,298
1,000
763
10×1,000 10×1,000

4.2 Compared Algorithms

We present a comparison of the Morfessor family (Creutz and Lagus 2002, 2005, 2007;
Kohonen, Virpioja, and Lagus 2010; Grönroos et al. 2014), the adaptor grammar frame-
arbeiten (Sirts and Goldwater 2013), and the conditional random fields (Ruokolainen
et al. 2013, 2014). These methods have freely available implementations for research
Zwecke.

The log-linear model presented by Poon, Cherry, and Toutanova (2009) is omitted
because it does not have a freely available implementation. Jedoch, the model has
been compared in the semi-supervised learning setting on Arabic and Hebrew with
CRFs and Morfessor previously by Ruokolainen et al. (2013). In these experiments, Die
model was substantially outperformed on both languages by the CRF method and on
Hebrew by Morfessor.

In order to provide a strong baseline for unsupervised learning results, we per-
formed preliminary experiments using the model presented by Lee, Haghighi, Und
Barzilay (2011).2 Their model learns segmentation in an unsupervised manner by
exploiting syntactic context of word forms observed in running text and has shown
promising results for segmentation of Arabic. In der Praxis, we found that when using the
method’s default hyperparameters, it did not yield nearly as good results as the other
unsupervised methods on our studied data sets. Adjusting the hyperparameters turns
out to be complicated by the computational demands of the method. When utilizing the
same computer set-up as for the other models, training the method requires limiting the
maximum word length of analyzed words to 12 in order for the model to fit in memory,
as well as requiring weeks of runtime for a single run. We decided to abandon further
experimentation with the method of Lee, Haghighi, and Barzilay (2011), as optimizing
its hyperparameters was computationally infeasible.

4.3 Evaluation

This section describes the utilized evaluation measures and the performed error
Analyse.

4.3.1 Boundary Precision, Recall, and F1-score. The word segmentations are evaluated by
comparison with reference segmentations using boundary precision, boundary recall,
and boundary F1-score. The boundary F1-score, or F1-score for short, equals the har-
monic mean of precision (the percentage of correctly assigned boundaries with respect

2 Implementation is available at http://people.csail.mit.edu/yklee/code.html.

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to all assigned boundaries) and recall (the percentage of correctly assigned boundaries
with respect to the reference boundaries):

Precision =

Recall =

C(correct)
C(proposed)

,

C(correct)
C(Referenz)

.

(4)

(5)

We follow Virpioja et al. (2011) and use type-based macro-averages. Jedoch, Wir
handle word forms with alternative analyses in a different fashion. Instead of penalizing
algorithms that propose an incorrect number of alternative analyses, we take the best
match over the alternative reference analyses (separately for precision and recall). Das
is because all the methods considered in the experiments provide a single segmentation
per word form.

Throughout the experiments, we establish statistical significance with confidence
Ebene 0.95, according to the standard one-sided Wilcoxon signed-rank test performed on
10 random subsets of 1,000 word forms drawn from the complete test sets (subsets may
contain overlapping word forms).

Because we apply a different treatment of alternative analyses, the results reported
in this article are not directly comparable to the boundary F1-scores reported for the
Morpho Challenge competitions (Kurimo et al. 2009; Kurimo, Virpioja, and Turunen
2010). Jedoch, the best boundary F1-scores for all languages reported in Morpho
Challenge have been achieved with the semi-supervised Morfessor Baseline algorithm
(Kohonen, Virpioja, and Lagus 2010), which is included in the current experiments.

4.3.2 Error Analysis. We next discuss the performed error analysis. The purpose of the
error analysis is to gain a more detailed understanding into what kind of errors the
methods make, and how the error types affect the overall F1-scores. Zu diesem Zweck, wir gebrauchen
a categorization of morphs into the categories PREFIX, STEM, and SUFFIX, in addition
defining a separate category for DASH. For the English and Finnish sections of the
Morpho Challenge data set, the segmentation gold standard annotation contain addi-
tional information for each morph, such as part-of-speech for stems and morphological
categories for affixes, which allows us to assign each morph into one of the morph
type categories. In some rare cases the tagging is not specific enough, and we choose
to assign the tag UNKNOWN. Jedoch, as we are evaluating segmentations, we lack
the morph category information for the proposed analyses. Folglich, we cannot
apply a straightforward category evaluation metric, such as category F1-score. In what
follows, we instead show how to use the categorization on the gold standard side to
characterize the segmentation errors.

We first observe that errors come in two kinds, over-segmentation and under-
segmentation. In over-segmentation, boundaries are incorrectly assigned within morph
segments, and in under-segmentation, the segmentation fails to uncover correct morph
boundaries. Zum Beispiel, consider the English compound word form girlfriend with a
correct analysis girl+friend. Dann, an under-segmentation error occurs in the event the
model fails to assign a boundary between the segments girl and friend. In der Zwischenzeit,
over-segmentation errors take place if any boundaries are assigned within the two
compound segments girl and friend, such as g+irl or fri+end.

As for the relationship between these two error types and the precision and re-
call measures in Equations (4) Und (5), we note that over-segmentation solely affects

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precision, whereas under-segmentation only affects recall. This is evident as the mea-
sures can be written equivalently as:

Precision =

C(proposed) − C(over-segm.)
C(proposed)

= 1 −

C(over-segm.)
C(proposed)

,

Recall =

C(Referenz) − C(under-segm.)
C(Referenz)

= 1 −

C(under-segm.)
C(Referenz)

.

(6)

(7)

In the error analysis, we use these equivalent expressions as they allow us to examine
the effect of reduction in precision and recall caused by over-segmentation and under-
segmentation, jeweils.

The over-segmentation errors occur when a segment that should remain intact
is split. Daher, these errors can be assigned into categories c according to the morph
tags PREFIX, STEM, SUFFIX, and UNKNOWN. The segments in the category DASH cannot
be segmented and do not, daher, contribute to over-segmentation errors. We then
decompose the precision and recall reductions in Equations (6) Und (7) into those caused
by errors in each category indexed by c and d:

Precision = 1 −

Recall = 1 −

C(over-segm. (C))
C(proposed)

,

C(under-segm. (D))
C(Referenz)

.

(cid:88)

C

(cid:88)

D

Gleichung (8) holds because

C(over-segm.)
C(Referenz)

=

(cid:80)

c C(over-segm. (C))
C(Referenz)

(cid:88)

=

C

C(over-segm. (C))
C(Referenz)

,

(8)

(9)

(10)

where c indexes the over-segmentation error categories. The expression for recall in
Gleichung (9) can be derived analogously, but it must be noted that the categorization d
by error type differs from that of precision as each under-segmentation error occurs at
a segment boundary, such as STEM-SUFFIX, STEM-STEM, PREFIX-STEM, rather than in the
middle of a segment. To simplify analysis, we have grouped all segment boundaries,
in which either the left or right segment category is DASH into the CONTAINS DASH
category. Boundary types that occur fewer than 100 times in the test data are merged
into the OTHER category.

Tisch 4 shows the occurrence frequency of each boundary category, averaged
over alternative analyses. Evidently, we expect the total precision scores to be most
influenced by over-segmentation of STEM and SUFFIX segment types because of their
hohe Frequenzen. Ähnlich, the overall recall scores are expected to be most impacted
by under-segmentation of STEM-SUFFIX and SUFFIX-SUFFIX boundaries. Finnish is also
substantially influenced by the STEM-STEM boundary, indicating that Finnish uses com-
pounding frequently.

For simplicity, when calculating the error analysis, we forgo the sampling procedure
of taking 10 × 1, 000 word forms from the test set, used for the overall F1-score for
statistical significance testing by Virpioja et al. (2011). Eher, we calculate the error
analysis on the union of these sampled sets. As the sampling procedure may introduce

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Tisch 4
Absolute and relative frequencies of the boundary categories in the error analysis. The numbers
are averaged over the alternative analyses in the reference annotation.

Category

STEM
SUFFIX
PREFIX
UNKNOWN

STEM-SUFFIX
SUFFIX-SUFFIX
STEM-STEM
SUFFIX-STEM
CONTAINS DASH
PREFIX-STEM
OTHER

English

Finnish

38,608.8
7,172.9
1,152.8
54.5

5,349.2
1,481.0
613.4
n/a
458.0
554.3
91.0

(82.2%)
(15.3%)
(2.5%)
(0.1%)

(62.6%)
(17.3%)
(7.2%)
n/a
(6.5%)
(5.4%)
(1.1%)

72,666.0
15,384.9
946.5
414.0

9,889.9
5,917.5
3,538.0
1,501.0
426.0
235.2
105.4

(81.3%)
(17.2%)
(1.1%)
(0.5%)

(45.8%)
(27.4%)
(16.4%)
(6.9%)
(2.0%)
(1.1%)
(0.5%)

the same word form in several samples, the error analysis precisions and recalls are not
necessarily identical to the ones reported for the overall results.

Zusammenfassend, although we cannot apply category F1-scores, we can instead catego-
rize each error by type. These categories then map directly to either reduced precision
or recall. Interpreting precision and recall requires some care as it is always possible to
reduce over-segmentation errors by segmenting less and, umgekehrt, to reduce under-
segmentation errors by segmenting more. Jedoch, if this is taken into account, Die
error categorization can be quite informative.

4.4 Model Learning and Implementation Specifics

4.4.1 Morfessor. We use a recently released Python implementation of the Morfessor
method (Virpioja et al. 2013; Smit et al. 2014).3 The package implements both the
unsupervised and semi-supervised Morfessor Baseline (Creutz and Lagus 2002, 2007;
Kohonen, Virpioja, and Lagus 2010). For Morfessor FlatCat we apply the Python imple-
mentation by Grönroos et al. (2014).4

In its original formulation, the unsupervised Morfessor Baseline uses no hyper-
Parameter. Jedoch, it was found by Virpioja, Kohonen, and Lagus (2011) that perfor-
mance does not improve consitently with growing data because the method segments
less on average for each added training word form. daher, we optimize the training
data size by including only the most frequent words in the following sizes: 10k, 20k,
30k, 40k, 50k, 100k, 200k, 400k, . . . , as well as the full set. We then choose the model
yielding highest F1-score on the development set.

As for semi-supervised training of Morfessor Baseline, we perform a grid search on
the development set for the hyperparameter β (see Section 3.3.1). For each value of β we
use the automatic adaptation of the hyperparameter α provided by the implementation.
The automatic adaptation procedure is applied during model training and is, daher,
computationally less demanding compared with grid search. Intuitively, the adaptation

3 Verfügbar unter https://github.com/aalto-speech/morfessor.
4 Verfügbar unter https://github.com/aalto-speech/flatcat.

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functions as follows. The hyperparameter α affects how much the method segments on
average. Although optimizing it for segmentation performance during training is non-
trivial, one can instead apply the heuristic that the method should neither over-segment
nor under-segment. daher, the implementation adjusts α such that the development
set precision and recall become approximately equal.

In the semi-supervised training for Morfessor FlatCat, the segmentations are ini-
tialized to the ones produced by the supervised CRF model trained with the same
amount of labeled training data. As automatic adaptation of the hyperparameter α has
not yet been implemented for Morfessor FlatCat, values for both α and β are found
by a combined grid search on the development set. The computational demands of
the grid search were reduced by using the optimal hyperparameter values for Mor-
fessor Baseline as an initial guess when constructing the grid. We also choose the non-
morpheme removal heuristics used by Morfessor FlatCat for each language separately
using the development set. For English, Estonian, and Finnish the heuristics described
by Grönroos et al. (2014) are beneficial, but they do not fit Turkish morphology as well.
For Turkish we convert non-morphemes into suffixes or stems, without modifying the
segmentation.

4.4.2 Adaptor Grammars. The technical details of the AG model are described by
Johnson, Griffiths, and Goldwater (2006) and the inference details are described by
Johnson, Griffiths, and Goldwater (2007). For unsupervised AG learning, we used the
freely available implementation,5 which was also the basis for the semi-supervised
implementation. Table label resampling was turned on and all hyperparameters were
inferred automatically as described by Johnson and Goldwater (2009). The metagram-
mar for AG Select is the same as described by Sirts and Goldwater (2013). Inductive
learning with the posterior grammar was done with a freely available CKY parser.6 For
both unsupervised and semisupervised AG, we use a three-level collocation-submorph
grammar in which the final segmentation is parsed out as a sequence of Morphs:

Word → Colloc+
Colloc → Morph+

Morph → SubMorph+

SubMorph → Char+

We experimented with two types of grammars, where the Word non-terminal is
either cached or not. These two grammar versions have no difference when trained
transductively. Jedoch, when training an inductive model, it may be beneficial to store
the subtrees corresponding to whole words because these trees can be used to parse the
words in the test set that were seen during training with a single rule. All models, beide
unsupervised and semi-supervised, are trained on 50k most frequent word types. Für
semi-supervised experiments, we upweight the labeled data by an integer number of
times by repeatedly caching the subtrees corresponding to morphemes in the annotated
Daten. The additional cached subtrees are rooted in the Morph non-terminal. Similarly to
semi-supervised Morfessor, we experimented with initializing the segmentations with

5 Available at http://web.science.mq.edu.au/~mjohnson/Software.htm.
6 Also obtained from http://web.science.mq.edu.au/~mjohnson/Software.htm.

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the output of the supervised CRF model, which in some cases resulted in improved
accuracy over the random initialization. We searched the optimal values for each ex-
periment for the upweighting factor, cached versus non-cached root non-terminal, Und
random versus CRF initialization on the development set.

An AG model is stochastic and each segmentation result is just a single sample from
the posterior. A common approach in such a case is to take several samples and report
the average result. Maximum marginal decoding (MMD) (Johnson and Goldwater 2009;
Stallard et al. 2012) that constructs a marginal distribution from several independent
samples and returns their mean value has been shown to improve the sampling-based
models’ results about 1–2 percentage points. Although the AG model uses sampling for
Ausbildung, the MMD is not applicable here because during test time the segmentations
are obtained using parsing. Jedoch, we propose another way of achieving the gain
in a similar range to the MMD. We train five different models and concatenate their
posterior grammars into a single joint grammar, which is then used as the final model to
decode the test data. Our experiments show that the posterior grammar concatentation,
similarly to the MMD, leads to consistent improvements of 1–2 percentage points over
the mean of the individual samples.

4.4.3 CRFs. The utilized Python implementation of the CRF model follows the presen-
tation of Ruokolainen et al. (2013, 2014).7 As for the left and right substring features
incorporated in the model, we include all substrings that occur in the training data. Der
maximum substring length and averaged perceptron learning of CRF model parameters
are optimized on the held-out development sets following Ruokolainen et al. (2013). Für
semi-supervised learning, we utilize log-normalized successor and predecessor variety
scores and binary Morfessor Baseline and AG features following the presentation of
Ruokolainen et al. (2014). The unsupervised Morfessor Baseline and AG models are
optimized on the development set as described earlier. The successor and predecessor
variety scores are estimated from all the available unannotated word forms apart from
words with a corpus frequency of one. The count cutoff is applied as a means of noise
reduction by removing peripheral phenomena, such as misspellings.

4.5 Ergebnisse

Here we summarize the results obtained using our experiment set-up. We present over-
all segmentation accuracies and error analysis in Sections 4.5.1 Und 4.5.2, jeweils.
We then discuss the results in Section 4.6.

4.5.1 Boundary Precisions, Recalls, and F1-scores. In what follows, we first review unsuper-
vised and supervised results and, subsequently, assess the semi-supervised results.

Segmentation accuracies using unsupervised and supervised methods are pre-
sented in Table 5. As for the supervised learning using the CRF model, we report
segmentation accuracies obtained using 100 Und 1,000 annotated word forms. Evidently,
utilizing annotated data provides a distinct advantage over learning from unannotated
Daten. Insbesondere, learning the supervised CRFs using 1,000 annotated word forms
results in substantially higher segmentation accuracies compared with learning in an
unsupervised manner from hundreds of thousands or millions of word forms. Tatsächlich,
using merely 100 annotated instances results in higher accuracies in English and Turkish

7 Available at http://users.ics.aalto.fi/tpruokol/.

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Tisch 5
Precision, recall, and F1-scores for unsupervised and supervised methods.

Method

Train (ann.)

Train (unann.)

Pre.

Rec.

F1

English
MORFESSOR BASELINE (USV)
AG (USV)

CRF (SV)
CRF (SV)
Estonian
MORFESSOR BASELINE (USV)
AG (USV)

CRF (SV)
CRF (SV)
Finnish
MORFESSOR BASELINE (USV)
AG (USV)

CRF (SV)
CRF (SV)
Turkish
MORFESSOR BASELINE (USV)
AG (USV)

0
0

100
1,000

0
0

100
1,000

0
0

100
1,000

0
0

384,903
384,903

0
0

3,908,820
3,908,820

0
0

2,206,719
2,206,719

0
0

617,298
617,298

76.3
62.2

86.0
91.6

76.4
78.4

79.2
88.4

70.2
68.1

73.0
88.3

67.9
72.7

76.3
84.4

72.7
81.2

70.4
73.4

59.1
76.7

51.9
68.1

59.4
79.7

65.8
76.5

76.3
71.7

78.8
86.1

73.3
75.8

67.7
82.1

59.7
68.1

65.5
83.8

66.8
74.6

CRF (SV)
CRF (SV)
The columns titled Train (unann.) denote the number of unannotated word forms utilized in
learning. The columns titled Train (ann.) denote the number of annotated word forms.

100
1,000

71.8
87.3

77.7
88.6

84.6
90.0

0
0

compared with the unsupervised methods. The balance between precision and recall
can be analyzed to assess how well the different methods are tuned to the amount of
segmentation present in the gold standard. As discussed in Section 4.3.2, high precision
in combination with low recall indicates under-segmentation, whereas high recall and
low precision indicates over-segmentation. Morfessor appears to favor precision over
recall (see Finnish) in the event a trade-off takes place. Im Gegensatz, the AG heavily favors
recall (see English). In der Zwischenzeit, the supervised CRF model consistently prefers higher
precision over recall.

These unsupervised and supervised learning results utilize the available data only
partially. Daher, we next discuss results obtained using semi-supervised learning—that
Ist, when utilizing all available annotated and unannotated word forms. The obtained
segmentation accuracies are presented in Table 6. We summarize the results as follows.
Erste, the semi-supervised CRF approach CRF (SSV) yielded highest segmentation ac-
curacies for all considered languages and data set sizes. The improvements over other
models are statistically significant. Compared with the supervised CRF model, the semi-
supervised extension successfully increases the recall while maintaining the high preci-
sion. As for the Morfessor family, MORF.FC (SSV) yields significantly higher F1-scores
compared with MORF.BL (SSV) on all languages. Jedoch, we found that without the
CRF initialization of MORF.FC (SSV), the performance gap decreases substantially (vgl.
similar results reported by Grönroos et al. [2014]). Andererseits, the variants
appear to behave in a similar manner in that, in the majority of cases, both approaches
increase the obtained precision and recall in a balanced manner compared with the
unsupervised approach MORF. BL (USV). In der Zwischenzeit, the AG variants AG (SSV) Und

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Precision, recall, and F1-scores for semi-supervised methods.

Method

Train (ann.)

Train (unann.)

Pre.

Rec.

F1

English
MORFESSOR BASELINE (SSV)
MORFESSOR FLATCAT (SSV)
AG (SSV)
AG SELECT (SSV)
CRF (SSV)

MORFESSOR BASELINE (SSV)
MORFESSOR FLATCAT (SSV)
AG (SSV)
AG SELECT (SSV)
CRF (SSV)
Estonian
MORFESSOR BASELINE (SSV)
MORFESSOR FLATCAT (SSV)
AG (SSV)
AG SELECT (SSV)
CRF (SSV)

MORFESSOR BASELINE (SSV)
MORFESSOR FLATCAT (SSV)
AG (SSV)
AG SELECT (SSV)
CRF (SSV)
Finnish
MORFESSOR BASELINE (SSV)
MORFESSOR FLATCAT (SSV)
AG (SSV)
AG SELECT (SSV)
CRF (SSV)

MORFESSOR BASELINE (SSV)
MORFESSOR FLATCAT (SSV)
AG (SSV)
AG SELECT (SSV)
CRF (SSV)
Turkish
MORFESSOR BASELINE (SSV)
MORFESSOR FLATCAT (SSV)
AG (SSV)
AG SELECT (SSV)
CRF (SSV)

MORFESSOR BASELINE (SSV)
MORFESSOR FLATCAT (SSV)
AG (SSV)
AG SELECT (SSV)
CRF (SSV)

100
100
100
100
100

1,000
1,000
1,000
1,000
1,000

100
100
100
100
100

1,000
1,000
1,000
1,000
1,000

100
100
100
100
100

1,000
1,000
1,000
1,000
1,000

100
100
100
100
100

1,000
1,000
1,000
1,000
1,000

384,903
384,903
384,903
384,903
384,903

384,903
384,903
384,903
384,903
384,903

3,908,820
3,908,820
3,908,820
3,908,820
3,908,820

3,908,820
3,908,820
3,908,820
3,908,820
3,908,820

2,206,719
2,206,719
2,206,719
2,206,719
2,206,719

2,206,719
2,206,719
2,206,719
2,206,719
2,206,719

617,298
617,298
617,298
617,298
617,298

617,298
617,298
617,298
617,298
617,298

81.7
83.6
69.0
75.9
87.6

84.4
86.9
69.8
76.7
89.3

77.0
81.8
71.8
60.9
81.5

80.6
84.7
67.1
62.8
90.2

69.8
77.6
65.5
66.8
80.0

76.0
81.6
69.7
69.4
89.3

76.6
80.2
74.1
69.0
81.3

85.1
84.9
77.0
70.5
89.3

82.8
83.0
85.8
79.4
81.0

83.9
85.2
87.1
82.3
87.0

76.1
74.5
75.5
90.4
82.1

80.7
82.0
88.8
90.3
86.3

70.8
73.6
70.5
73.6
77.4

78.0
80.2
77.6
74.3
87.9

80.5
83.9
82.8
82.3
86.0

89.4
92.2
90.9
80.4
92.0

82.2
83.3
76.5
77.6
84.2

84.1
86.0
77.5
79.4
88.1

76.5
77.9
73.6
72.8
81.8

80.7
83.3
76.4
74.1
88.2

70.3
75.5
67.9
70.0
78.7

77.0
80.9
73.4
71.8
88.6

78.5
82.0
78.2
75.0
83.5

87.2
88.4
83.4
75.1
90.7

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AG SELECT (SSV) heavily favor recall over precision, indicating over-segmentation.8
zuletzt, in contrast with the unsupervised learning results, in the semi-supervised setting
the AG framework is significantly outperformed by the Morfessor variants.

4.5.2 Error Analysis. Nächste, we examine how different error types contribute to the ob-
tained precision and recall measures, Und, consequently, the overall F1-scores. To this
end, we discuss the error analyses for English and Finnish presented in Tables 7 Und 8,
jeweils.

Baselines. The first two lines in Tables 7 Und 8 present the baseline models WORDS and
LETTERS. The WORDS model corresponds to an approach in which no segmentation is
durchgeführt, das ist, all the word forms are kept intact. The LETTERS approach assigns a
segment boundary between all adjacent letters. These approaches maximize precision
(WORDS) and recall (LETTERS) at the cost of the other. Mit anderen Worten, no model can
produce more over-segmentation errors compared with LETTERS, and no model can
produce more under-segmentation errors compared with WORDS.9

Given the baseline results, we observe that the overall precision scores are most
influenced by over-segmentation of STEM and SUFFIX segment types because of their
hohe Frequenzen. Ähnlich, the overall recall scores are most impacted by under-
segmentation of STEM-SUFFIX and SUFFIX-SUFFIX boundaries. Finnish recall is also
substantially influenced by the STEM-STEM boundary, indicating that Finnish uses
compounding frequently.

Morfessor. Similarly to the baseline (WORDS and LETTERS) results, the majority of over-
segmentation errors yielded by the Morfessor variants take place within the STEM and
SUFFIX segments, and most under-segmentation errors occur at the STEM-SUFFIX and
SUFFIX-SUFFIX boundaries. When shifting from unsupervised learning using MORF.BL
(USV) to semi-supervised learning using MORF.BL (SSV) and MORF.FC (SSV), the over-
segmentation problems are alleviated rather substantially, resulting in higher over-
all precision scores. Zum Beispiel, consider the word form countermanded, for which
MORF.BL (SSV) assigns the correct segmentation countermand+ed, but which is severely
oversegmented by MORF.BL (USV) as counter+man+d+ed. One also observes a dramatic
increase in the overall recall scores, indicating a smaller amount of under-segmentation
taking place. Zum Beispiel, consider the word form products, for which MORF.BL (SSV)
assigns the correct segmentation product+s, but for which MORF.BL (USV) assigns no
boundaries. Jedoch, the under-segmentation errors do not decrease consistently:
Although the STEM-SUFFIX and SUFFIX-SUFFIX errors are decreased substantially, eins
additionally observes a decline or no change in the model’s ability to uncover STEM-
STEM and PREFIX-STEM boundaries.

8 Generally, in the presence of annotated training data, under-segmentation and over-segmentation can be
avoided by explicitly tuning the average level of segmentation. Such tuning is performed for Morfessor
with the weighted objective function and for AG by choosing the level in the parse tree from which to
extract the segmentations. By default, the AG segmentations were extracted from the Morph level as this
gave the highest score on the development set. Jedoch, the Estonian segmentations are extracted from
the Colloc level, which also explains why in the Estonian case the precision is higher than recall. Diese
results suggest that AG (SSV) may benefit from yet another layer in the grammar, which would help to
learn a better balance between precision and recall.

9 Intuitively, WORDS should yield zero recall. Jedoch, when applying macro averaging, a word having a

gold standard analysis with no boundaries yields a zero denominator and is therefore undefined. To
correct for this, we interpret such words as having recall 1, which explains the non-zero recall for WORDS.

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Tisch 7
Error analysis for English.

Over-Segmentation

Under-Segmentation

Method

WORDS
LETTERS

MORF.BL (USV)
MORF.BL (SSV)
MORF.FC (SSV)
AG (USV)
AG (SSV)
AG SELECT (SSV)
CRF (SV)
CRF (SSV)

M
E
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S

0.0
71.1

20.6
14.3
11.2
31.8
27.8
18.4
7.3
9.6

X
ICH
F
F
U
S

0.0
11.8

2.9
1.3
1.7
5.8
2.1
4.8
0.9
0.8

X
ICH
F
E
R
P

0.0
1.7

0.0
0.1
0.0
0.1
0.1
0.0
0.1
0.0

N
W
Ö
N
K
N
U

0.0
0.3

0.1
0.0
0.1
0.0
0.1
0.1
0.0
0.1

L
A
T
Ö
T

/

E
R
P

100.0
15.1

76.4
84.4
87.1
62.3
70.0
76.6
91.8
89.5

X
ICH
F
F
U
S
–

M
E
T
S

55.1
0.0

17.0
9.8
8.6
10.6
10.1
8.2
10.4
8.4

X
ICH
F
F
U
S
–
X
ICH
F
F
U
S

8.6
0.0

4.7
0.6
0.5
3.5
1.4
1.4
0.5
0.5

M
E
T
S
–

M
E
T
S

5.9
0.0

0.6
2.8
2.2
0.1
0.2
2.2
4.2
1.4

M
E
T
S
–
X
ICH
F
E
R
P

4.4
0.0

1.1
2.1
2.5
0.7
0.6
4.1
2.9
1.9

H
S
A
D
S
N

ICH

A
T
N
Ö
C

2.5
0.0

0.0
0.0
0.1
0.2
0.2
1.5
0.1
0.0

L
A
T
Ö
T

/

C
E
R

23.1
100.0

76.4
84.3
85.5
84.7
87.3
82.2
81.5
87.4

R
E
H
T
Ö

0.6
0.0

0.2
0.4
0.4
0.2
0.2
0.4
0.4
0.4

Over-segmentation and under-segmentation errors reduce precision and recall, jeweils. Für
Beispiel, the total precision of MORF. BL (USV) is obtained as 100.0 − 20.6 − 2.9 − 0.0 − 0.1 = 76.4.
The lines MORF. BL (USV), MORF. BL (SSV), and MORF. FC (SSV) correspond to the unsupervised
Morfessor Baseline, semi-supervised Morfessor Baseline, and semi-supervised Morfessor FlatCat
Modelle, jeweils.

Tisch 8
Error analysis for Finnish.

Over-Segmentation

Under-Segmentation

Method

WORDS
LETTERS

MORF.BL (USV)
MORF.BL (SSV)
MORF.FC (SSV)
AG (USV)
AG (SSV)
AG SELECT (SSV)
CRF (SV)
CRF (SSV)

M
E
T
S

0.0
65.2

26.6
20.8
15.3
28.3
27.9
24.2
9.3
9.2

X
ICH
F
F
U
S

0.0
13.8

3.4
2.9
2.9
3.3
2.1
6.1
2.3
1.4

X
ICH
F
E
R
P

0.0
0.7

0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0

N
W
Ö
N
K
N
U

0.0
0.6

0.2
0.2
0.1
0.2
0.2
0.1
0.0
0.1

L
A
T
Ö
T

/

E
R
P

100.0
19.7

69.7
76.1
81.7
68.1
69.7
69.5
88.3
89.3

X
ICH
F
F
U
S
–

M
E
T
S

49.2
0.0

28.8
13.6
12.2
19.0
14.7
13.2
10.7
8.0

X
ICH
F
F
U
S
–
X
ICH
F
F
U
S

21.8
0.0

17.1
5.9
5.2
11.5
6.5
7.8
2.2
2.3

M
E
T
S
–

M
E
T
S

17.2
0.0

1.7
1.9
1.5
0.7
0.7
2.4
5.8
1.2

M
E
T
S
–
X
ICH
F
F
U
S

4.8
0.0

0.5
0.5
0.6
0.2
0.2
1.1
1.1
0.4

H
S
A
D
S
N

ICH

A
T
N
Ö
C

1.4
0.0

0.0
0.0
0.1
0.3
0.1
0.8
0.1
0.1

M
E
T
S
–
X
ICH
F
E
R
P

1.0
0.0

0.1
0.1
0.1
0.0
0.1
0.2
0.3
0.1

L
A
T
Ö
T

/

C
E
R

4.1
100.0

51.6
78.0
80.2
68.1
77.6
74.4
79.7
87.8

R
E
H
T
Ö

0.6
0.0

0.2
0.1
0.1
0.2
0.1
0.1
0.2
0.2

Over-segmentation and under-segmentation errors reduce precision and recall, jeweils. Der
lines MORF. BL (USV), MORF. BL (SSV), and MORF. FC (SSV) correspond to the unsupervised
Morfessor Baseline, the semi-supervised Morfessor Baseline, and semi-supervised Morfessor
FlatCat models, jeweils.

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Adaptor Grammars. Similarly to the baseline and Morfessor results, the majority of
over-segmentation errors yielded by the AG variants occur within the STEM and
SUFFIX segments. Compared with the unsupervised AG (USV) Ansatz, der Erste
semi-supervised extension AG (SSV) manages to reduce over-segmentation of the STEM
segments slightly and SUFFIX segments substantially, thus resulting in overall higher
precision. In der Zwischenzeit, the second extension AG SELECT (SSV) also results in overall
higher precision by reducing over-segmentation of STEM segments substantially—
although, for Finnish, SUFFIX is oversegmented compared with AG SELECT (USV).
Andererseits, whereas both AG (SSV) and AG SELECT (SSV) improve recall
on Finnish compared to AG (USV), only AG (SSV) succeeds in improving recall for
English. This is because the AG SELECT (SSV) variant decreases the model’s ability
to capture other than STEM-SUFFIX and SUFFIX-SUFFIX boundaries compared with the
unsupervised AG (USV) Ansatz.

Conditional Random Fields. In contrast to the Morfessor and AG frameworks, the error
patterns produced by the CRF approach do not directly follow the baseline approaches.
Insbesondere, we note that the supervised CRF (SV) approach successfully captures
SUFFIX-SUFFIX boundaries and fails to find STEM-STEM boundaries—that is, behaves in
an opposite manner compared with the baseline results. CRF (SV) also under-segments
the less-frequent PREFIX-STEM and STEM-SUFFIX boundaries for English and Finnish,
jeweils. In der Zwischenzeit, the semi-supervised extension CRF (SSV) alleviates the prob-
lem of finding STEM-STEM boundaries substantially, resulting in improvement in overall
recall. Zum Beispiel, CRF (SSV) correctly segments compound forms rainstorm and wind-
pipe as rain+storm and wind+pipe, whereas CRF (SV) incorrectly assigns no segmentation
boundaries to either of these forms. Note that improving recall means that CRF (SSV)
is required to segment more compared with CRF (SV). For English, this increased seg-
mentation results in a slight increase in over-segmentation of STEM—that is, the model
trades off the increase in recall for precision. Zum Beispiel, whereas CRF (SV) correctly
segments ledgers as ledger+s, CRF (SSV) yields an incorrect segmentation led+ger+s.

4.6 Diskussion

When increasing the amount of data utilized for learning—that is, when shifting from
fully unsupervised or supervised learning to semi-supervised learning—we naturally
expect the segmentation method families to improve their performance measured using
the F1-score. In der Tat, as shown in Tables 5 Und 6, this improvement takes place within
all considered approaches. In manchen Fällen, as exemplified by the CRF model on English,
achieving a higher F1-score may require a trade-off between precision and recall—that
Ist, the model lowers precision somewhat to gain recall (or vice versa). Jedoch, von
examining the error analyses in Tables 7 Und 8, we also observe the occurrence of a
second kind of trade-off, in which the semi-supervised Morfessor and AG approaches
trade off under-segmentation errors to other under-segmentation errors. Insbesondere,
although the STEM-SUFFIX and SUFFIX-SUFFIX boundary recall errors are decreased, eins
also observes an increase in the errors at STEM-STEM and PREFIX-STEM boundaries.
This type of behavior indicates an inherent inefficiency in the models’ ability to utilize
increasing amounts of data.

Nächste, we discuss potential explanations for the empirical success of the discrimina-
tively trained CRF approach. Erste, discriminative training has the advantage of directly
optimizing segmentation accuracy with few assumptions about the data generating pro-
Prozess. In der Zwischenzeit, generative models can be expected to perform well only if the model

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definition matches the data-generating process adequately. Allgemein, discriminative
approaches should generalize well under the condition that a sufficient amount of train-
ing data is available. Given the empirical results, this condition appears to be fulfilled
for morphological segmentation in the minimally supervised setting. Zweite, the CRFs
aim to detect boundary positions based on rich features describing substring contexts.
Because the substrings are more frequent than lexical units, their use enables more
efficient utilization of sparse data. Zum Beispiel, consider a training data that consists
of a single labeled word form kato+lla (on roof ). When segmenting an unseen word form
matolle (onto rug), with the correct segmentation mato+lle, the CRFs can utilize the famil-
iar left and right substrings ato and ll, jeweils. Im Gegensatz, a lexicon-based model
has a lexicon of two morphs {kato, lla}, neither of which match any substring of matolle.
Endlich, we discuss how the varying approaches differ when learning to split affixes
and compounds. To this end we first point out that, in the examined English and
Finnish corpora, the suffix class is closed and has only a small number of morphemes
compared with the open prefix and stem categories. In consequence, a large coverage
of suffixes should be achievable already with a relatively small annotated data set. Das
observation is supported by the evident success of the fully supervised CRF method
in learning suffix splitting for both considered languages. Andererseits, although
more efficient at learning suffix splitting, the supervised CRF approach is apparently
poor at detecting compound boundaries. Intuitively, learning compound splitting in a
supervised manner seems infeasible because the majority of stem forms are simply not
present in the available small annotated data set. In der Zwischenzeit, the semi-supervised CRF
extension and the generative Morfessor and AG families, which do utilize the large
unannotated word lists, capture the compound boundaries with an appealing high ac-
curacy. This result again supports the intuition that in order to learn the open categories,
one is required to utilize large amounts of word forms for learning. Jedoch, it appears
that the necessary information can be extracted from unannotated word forms.

5. Future Work

In diesem Abschnitt, we discuss our findings on potentially fruitful directions for future
Forschung.

5.1 On Improving Existing Approaches

Interessant, the CRF-based segmentation method achieves its success using mini-
malistic, language-independent features with a simple, feature-based, semi-supervised
learning extension. daher, it seems plausible that one could boost the accuracy fur-
ther by designing richer, language-dependent feature extraction schemes. Zum Beispiel,
one could potentially exploit features capturing vowel harmony present in Finnish,
Estonian, and Turkish. As for semi-supervised learning, one can utilize unannotated
word lists in a straightforward manner by using the feature set expansion approach as
discussed by Ruokolainen et al. (2014). Similar expansion schemes for CRFs have also
been successfully applied in the related tasks of Chinese word segmentation (Sun and
Xu 2011; Wang et al. 2011) and chunking (Turian, Ratinov, and Bengio 2010). Neverthe-
weniger, there exist numerous other approaches proposed for semi-supervised learning of
CRFs (Jiao et al. 2006; Mann and McCallum 2008; Wang et al. 2009) that could poten-
tially provide an advantage over the feature-based, semi-supervised learning approach.
Naturally, one could also examine utilizing these techniques simultaneously with the
expanded feature sets.

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As discussed in Section 3.3, it is possible for the generative models to utilize an-
notated data in a straightforward manner by fixing samples to their true values. Das
approach was taken by Poon, Cherry, and Toutanova (2009), Spiegler and Flach (2010),
and Sirts and Goldwater (2013). Andererseits, as discussed in Section 3.3.1, für
the Morfessor family the fixing approach was outperformed by the weighted objective
Funktion (Kohonen, Virpioja, and Lagus 2010). It has been shown that the weighting
can compensate for a mismatch between the model and the data generating process
(Cozman et al. 2003; Cozman and Cohen 2006; Fox-Roberts and Rosten 2014). daher,
it would appear to be advantageous to study weighting schemes in combination with
all the discussed generative models.

5.2 On Potential Novel Approaches

Based on the literature survey presented in Section 3.3.2, one can observe that there
exists substantial work on generative lexicon-based approaches and methods based
on discriminative boundary detection. Im Gegensatz, there exists little to no research on
models utilizing lexicons and discriminative learning or generative boundary-detection
approaches. Zusätzlich, as mentioned in Section 3.3.2, so far there has been little work
discussing a combination of lexicon-based and boundary detection approaches. It could
be fruitful to explore these modeling aspects further in the future.

6. Conclusions

We presented a comparative study on data-driven morphological segmentation in a
minimally supervised learning setting. In this setting the segmentation models are
estimated based on a small amount of manually annotated word forms and a large set
of unannotated word forms. In addition to providing a literature survey on published
Methoden, we presented an in-depth empirical comparison on three diverse model fam-
ilies. The purpose of this work is to extend the existing literature with a summarizing
study on the published methodology as a whole.

Based on the literature survey, we concluded that the existing methodology con-
tains substantial work on generative lexicon-based approaches and methods based on
discriminative boundary detection. As for which approach has been more successful,
both the previous work and the empirical evaluation presented here strongly imply that
the current state of the art is yielded by the discriminative boundary detection method-
ology. Allgemein, our analysis suggested that the models based on generative lexicon
learning are inefficient at utilizing growing amounts of available data. In der Zwischenzeit, Die
studied discriminative boundary detection method based on the CRF framework was
successful in gaining consistent reduction in all error types, given increasing amounts of
Daten. zuletzt, there exists little to no research on models utilizing lexicons and discrimi-
native learning or generative boundary-detection approaches. Studying these directions
could be of interest in future work.

Danksagungen
This work was financially supported
by Langnet (Finnish doctoral programme in
language studies), the Academy of Finland
under the Finnish Centre of Excellence
Program 2012–2017 (grant no. 251170)
and LASTU Programme (grants no. 256887
Und 259934), project Multimodally grounded

language technology (grant no. 254104), Und
the Tiger University program of the Estonian
Information Technology Foundation for
Education.

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