Quantifying Cognitive Factors in Lexical Decline
David Francis1
Ella Rabinovich1
Farhan Samir1
David Mortensen2
Suzanne Stevenson1
1Department of Computer Science, University of Toronto, Canada
2Language Technologies Institute, Carnegie Mellon University, Etats-Unis
{dfrancis, ella, fsamir, suzanne}@cs.toronto.edu
dmortens@cs.cmu.edu
Abstrait
We adopt an evolutionary view on language
change in which cognitive factors (in addition
to social ones) affect the fitness of words and
their success in the linguistic ecosystem. Spe-
cifically, we propose a variety of psycholin-
guistic factors—semantic, distributional, et
phonological—that we hypothesize are predic-
tive of lexical decline, in which words greatly
decrease in frequency over time. Using his-
torical data across three languages (English,
French, and German), we find that most of our
proposed factors show a significant difference
in the expected direction between each curated
set of declining words and their matched stable
words. De plus, logistic regression analyses
show that semantic and distributional factors
are significant in predicting declining words.
Further diachronic analysis reveals that de-
clining words tend to decrease in the diversity
of their lexical contexts over time, gradually
narrowing their ‘ecological niches’.
1
Introduction
Many researchers, from Schleicher (1863) up to
the present (Croft, 2000; Oudeyer and Kaplan,
2007; Atkinson et al., 2008; Thanukos, 2008;
Turney and Mohammad, 2019), have drawn analo-
gies between biological evolution and the evolu-
tion of languages—their structure, their semantics,
and their lexicons. Lexically speaking, as Schle-
icher first pointed out, diachrony can be viewed as
a struggle for survival by individual words whose
propagation into future generations is contingent
on their continued fitness for one or more niches
in the ecology of the speech community—as
determined by a host of factors. Here we study the
question of lexical decline—a gradual decrease in
frequency and ultimate obsolescence of words.
What explains that the word ‘amusements’ has
declined in the last 200 années, but ‘foundations’
has not, as shown in Figure 1 (along with other
similar pairs)? Social factors clearly play a role,
as changes in culture and technology may lead
words to fall in and out of use. But cognitive and
linguistic factors also influence lexical survival
(par exemple., Vejdemo and H¨orberg, 2016). Words that
are semantically similar to many other words may
come to be used less because of intense compe-
tition in the cognitive process of lexical access
(Chen and Mirman, 2012). Words that can occupy
many niches, distributionally speaking, should
have better chances of being learned and used,
and therefore perpetuated, than words that are
confined to a narrow range of contexts or senses
(Altmann et al., 2011; Stewart and Eisenstein,
2018). On the other hand, words that are pho-
nologically very different from other words may
suffer because they are more difficult to access,
sitting as they do at the formal fringes of the men-
tal lexicon (Edwards et al., 2004). Autrement dit,
we suggest that semantic, distributional, and pho-
nological factors all play a role in the natural
selection of words.
While attention to predicting which words will
emerge, live, and die goes back to Schleicher,
there is relatively little computational work on this
sujet (examples include Cook and Stevenson,
2010; Hamilton et al., 2016; Xu et al., 2019;
Ryskina et al., 2020). En particulier, little attention
has been paid to the factors that contribute to lexi-
cal decline (but see Vejdemo and H¨orberg [2016]
for related work on lexical replacement). Ce
is unfortunate because understanding this phe-
nomenon answers an important scientific question
about language change—how lexicons become as
ils sont. We ground these phenomena in an evo-
lutionary model of linguistic diachrony in which
fitness is influenced by independently motivated
cognitive processes like lexical access.
Our study spans 20 decades and three languages.
We find that there are consistent factors—semantic,
1529
Transactions of the Association for Computational Linguistics, vol. 9, pp. 1529–1545, 2021. https://doi.org/10.1162/tacl a 00441
Action Editor: Jacob Eisenstein. Submission batch: 3/2021; Revision batch: 8/2021; Published 12/2021.
c(cid:2) 2021 Association for Computational Linguistics. Distributed under a CC-BY 4.0 Licence.
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of predictability and distinctiveness: They effi-
ciently recombine elements from existing word
forms, yet are sufficiently distinctive to reduce
confusion. Viewing the lexicon as an evolving
ecosystem, with interacting birth and death of
words, we hypothesize that analogous factors will
play a role in lexical decline as in neology.
Compared to research on neology, the work on
lexical obsolescence and loss is relatively sparse.
While the study of neology often draws on occur-
rence of new word forms in contemporary corpora,
in contrast, the study of lexical loss inherently
relies on the availability (and the quality) of large
diachronic textual resources. Tich`y (2018) pro-
poses a methodology for identifying declining
words in such a corpus, and performs qualitative
analysis of a sample of such words, focusing on
spelling standardization and changes in word-
formation strategies. Using the Google-books
dataset (Michel et al., 2011), Petersen et al. (2012)
study the ‘death rate’ of words primarily stem-
ming from misspellings and print errors typical to
historical corpora, focusing on the rate and not the
causes of linguistic decline.
Other work touches on lexical decline less di-
rectly, but explores potential predictive factors (comme
we do) in related processes—factors that may also
play a role in decline. Hamilton et al. (2016) con-
sider the factors that influence meaning shift—rise
and decline of meanings within a word (plutôt que
of words themselves)—and find that both word
frequency and number of meanings play a role.
Turney and Mohammad (2019) track the evolu-
tion of 4K English synsets, attempting to predict
a synset ‘leader’—the member of the synset with
highest frequency. They find the current ‘leader-
ship’ of a word to be the most predictive factor of
its future status as a ‘leader’, again illustrating the
driving force of frequency in lexical status. Comment-
jamais, while a word may become a synset leader
at the expense of other words, this work does not
perform a systematic study of factors predictive
of lexical decline.
Enfin, Vejdemo and H¨orberg (2016) con-
ducted a study of lexical replacement—a closely-
related but narrower phenomenon than lexical
decline—exploring similar semantic factors to
those we investigate here using a markedly dif-
ferent methodology. Their study is focused on a
small set of core vocabulary in Indo-European lan-
guages (‘‘Swadesh list’’ words, Swadesh [1952],
from Pagel et al. [2007]). Our research here
Chiffre 1: Matched declining:stable (dec:stb) word pairs
illustrated by dark (dec) and light (stb) shade of the
same color.
distributional, and phonological—that predict
whether a word is likely to substantially decline
in frequency. We propose that our observations
are consistent with a model where there is a feed-
back loop between cognition and usage driving
the diachronic development of lexicons.1
2 Related Work
There is a vast body of research on lexical change
of various kinds; in this section we focus on work
involving the birth and death of words, as it is
most closely related to our study here.
Lexical neology—introduction of new words—
is one of the most evident types of lexical change.
Various computational studies have suggested a
range of factors underlying the phenomenon of
neology, including semantic, distributional, et
phonological influences. Ryskina et al. (2020)
show that lexical neology can be partly explained
by the factor of supply—new words tend to emerge
in areas of semantic space where they are needed
most, c'est à dire., areas exhibiting relative sparsity. Draw-
ing on theories of patterns of word growth
(Metcalf, 2004; Cook and Stevenson, 2010;
Chesley and Baayen, 2010), additional studies
suggest that (among other factors) greater linguis-
tic distribution across individuals and topics plays
a significant positive role in the fate of novel
lexical items in online forums (Altmann et al.,
2011; Stewart and Eisenstein, 2018). Consider-
ing phonological factors, Xu et al. (2019) show
that new words emerge under the joint constraints
1All data and code is available at https://github
.com/ellarabi/linguistic_decline.
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addresses a much broader phenomenon of gen-
eral lexical decline, and proposes a wider range
of factors influencing that process.
3 Overview of Our Approach
Motivated by the perspective of the lexicon as an
evolving ecosystem, in which words are subject
to various cognitive pressures that can influence
their ‘survival’, we aim here to identify factors that
may be indicative of words that are likely to de-
cline. Spécifiquement, we propose factors that, quand
calculated at a given time in history, t (in our
étude, 1800–1810), are hypothesized to be predic-
tive of lexical decline during a subsequent stretch
of time, up to t+n (here 2000–2010).
First we note one potential factor whose in-
fluence on decline is not explored here: that of
a word’s frequency. Having seen that (relatively
higher) frequency of a word is the single best
predictor of future (relatively higher) frequency
(Turney and Mohammad, 2019), a natural hypo-
thesis is that lower frequency may conversely be
predictive of future decline. Cependant, since rela-
tively low frequency may indicate a word already
‘on its way out’, we instead control for frequency:
Given words of similar frequency, we explore
what other properties are most predictive of which
will subsequently decline and which survive.
As noted in §1, we consider that semantic,
distributional, and phonological factors all may
play a role in lexical decline, due to their influence
on the ease or difficulty of learning and accessing
of words, which may impact a word’s continued
role in the lexical ecosystem. Here we provide
the motivation for the factors we consider; §4.3
provides detail on how they are computed. While
this discussion may suggest causal relationships
(par exemple., words decline because their lexical access
is more difficult), our subsequent analyses focus
on correlations of the factors with decline, and are
thus agnostic with respect to causality.
We consider several semantic factors, drawing
on inspiration from the acquisition and processing
littératures. D'abord, we consider the role of the se-
mantic space a word occurs in. While some work
has found that lexical access is facilitated by hav-
ing dense semantic neighborhoods (having many
closely related words) (Buchanan et al., 2001),
other work has noted their inhibitory effect on se-
mantic processing (Mirman and Magnuson, 2008),
in line with findings of inhibitory competition
in phonological neighborhoods (Marslen-Wilson,
1990; Dahan et al., 2001). Such inhibitory ef-
fects may underlie the observation that words in
semantically dense (c'est à dire., more competitive) envi-
ronments are more likely to be driven out, to the
benefit of others that can potentially be used to
express roughly the same meaning (par exemple., Br´eal,
1897; Vejdemo and H¨orberg, 2016). Ainsi, sim-
ilarly to Ryskina et al. (2020), we estimate the
density of a word’s immediate semantic neigh-
bourhood, where we predict words with a higher
semantic density to be more likely to decline.
Suivant, we consider properties of the semantics
of the word itself. Psycholinguistic studies have
found that more concrete words—roughly, ceux
referring to a perceptible entity—are learned and
retrieved more easily (par exemple., James, 1975; De Groot
and Keijzer, 2000). De plus, concrete words
may form a more stable subset of the lexicon
(Swadesh, 1971; cf. a similar finding in Vejdemo
and H¨orberg [2016] using imageability ratings, un
notion that is highly correlated with concreteness).
Because words conveying a more concrete mean-
ing appear more likely to survive, we consider the
level of concreteness of a word as a second se-
mantic factor, where lower concreteness predicts
a higher chance of decline.
En plus, having a higher degree of poly-
semy has been shown to have a facilitatory effect
on a word’s lexical access, due to multiple related
senses contributing to aggregate activation of the
word (Jastrzembski, 1981; Rood et al., 2002).
Access to a word across many senses may sim-
ilarly lead to greater survivability (Vejdemo and
H¨orberg, 2016), and we thus predict that words
with a higher number of meanings will be less
likely to fall out of use in a language.
In addition to the influence of semantic prop-
erties, others have proposed a central role for
distributional factors in lexical learning and pro-
cessation (McDonald and Shillcock, 2001; Jones
et coll., 2017). En particulier, words that occur in
more varied contexts are easier both to learn
(Johns et al., 2016) and to access (McDonald and
Shillcock, 2001). En outre, words with broader
topical dissemination tend to become more ro-
bustly entrenched into the lexicon (Altmann
et coll., 2011; Stewart and Eisenstein, 2018); con-
versely, we expect that words that occur in a nar-
rower range of contexts will be more apt to fall
out of use. This too follows our lexical evolu-
tion perspective: Just as species that can occupy
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many niches in a natural ecology are more likely
to survive, generation to generation, lexemes that
occupy many niches in the linguistic ecology are
less likely to face extinction (or decline). We adopt
the distributional factor of contextual diversity
to model this fact.
Like semantic and distributional effects, phono-
logical effects are also known to interact, in a
complex way, with lexical processing, and we hy-
pothesize that such factors may also be predictive
of lexical decline. Par exemple, psycholinguistic
studies have found that phonotactically typical
words are recognized more quickly than atypical
words (Vitevitch et al., 1999). We correspond-
ingly predict that phonological typicality will be
associated with lower rates of lexical decline.2
As with semantic neighborhoods, psycholin-
guistic experiments have also found mixed effects
of phonological neighborhoods on lexical process-
ing: both competition among similar phonologi-
cal forms (as noted above, Marslen-Wilson, 1990;
Dahan et al., 2001), as well as potential facilitation
from having a higher number of phonologically-
close neighbors (Yates et al., 2004; Vitevitch,
2002; Marian and Blumenfeld, 2006). Given a
preponderance of evidence of facilitatory effects
on lexical processing, we predict that phono-
logical density will be inversely correlated with
lexical decline.3
Enfin, we predict that words with greater pho-
nological complexity—for our purposes, longer
in terms of the number of syllables—will be more
likely to decline. This hypothesis follows from the
speculation that words are processed as sequences
of syllables rather than sequences of phonemes,
and that longer words are more effortful to pro-
cess. Spécifiquement, we hypothesize that words with
higher number of syllables (per phoneme) will be
more likely to decline.
Tableau 1 summarizes the seven proposed factors,
grouped by categories, as well as their predicted
2We also considered orthographic typicality; this mea-
sure correlated highly with phonological typicality (r of over
0.6 in all 3 languages), and showed precisely the same pattern
as phonological typicality across declining and stable words.
3The mixed effects of competition and facilitation within
a phonological neighborhood may help explain why, as noted
earlier, new word forms tend to show a tension between
predictability and distinctiveness (Xu et al., 2019). Here we
predict an inverse correlation of phonological density and
lexical decline, but future research on the role of neighbor-
hoods in lexical access will be necessary to reconcile these
viewpoints.
Groupe
Factor
semantic
semantic density
concreteness
number of meanings
distributional contextual diversity
phon typicality
phonological phon density
phon complexity
Predicted Corr.
w/Decline
+
−
−
−
−
−
+
Tableau 1: Factors and their predicted correlation,
positive (+) or negative (−), with decline.
direction of correlation with the tendency of a
word to decline. Note that none of these factors
operates in isolation, and they may interact to push
in the same or different directions; Par exemple,
a word with competition from many semantically
similar alternatives may also be highly phonolog-
ically typical or simple. To be clear, we are not
claiming that these are the only factors predic-
tive of the decline of words. Par exemple, other
linguistic factors, such as pragmatic influences,
are likely involved, but we limit our study to lex-
ical properties that are readily extractable from
the available historical resources such as corpora
and dictionaries. De plus, such cognitive factors
necessarily interact with extensive sociological
and cultural trends that impact word usage (par exemple.,
the decline in systems of aristocracy, or a shift in
medical terminology). Here we explore whether
internal cognitive factors may play a role beyond
these broad extra-linguistic influences.
In order to assess the factors both individu-
ally and as a collection, we perform two kinds of
analyses. We identify a set of words that decline
in usage over a 200-year period, and pair those
with a set of words that are stable in frequency over
the same period. We first consider whether the val-
ues (in the initial decade) of each of these proposed
factors differs in the expected direction between
the declining and stable words. Suivant, we see
which factors may be most explanatory of decline
when the set of 7 factors are used collectively in
a logistic regression analysis. In §4 we describe
how we select our declining and stable words, et
estimate the above factors, and in §5 we present
the results of these two analyses. We follow this in
§6 with further diachronic analysis of the pattern
of contextual usage in how words decline.
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4 Materials and Methods
Our goal is to explore whether the factors iden-
tified above can indeed distinguish words at a
time t that will decline over a subsequent period
of time t + n, from words that remain relatively
stable over that same time period. To this end,
we develop measures to identify a set of words
that have declined over a historical period, and a
set of stable words for comparison. Cependant, nous
cannot form our experimental word sets by sim-
ply selecting words randomly from each of these
lists. Because confounding lexical properties (tel
as frequency) may interact with our identified
factors of interest, we must adopt a more con-
trolled approach, standard in cognitive research,
of matching our declining and stable words on a set
of potential confounds. In §4.1, we first motivate
our approach to forming our experimental items –
pairs of declining and stable words matched on
covariate properties. We then detail how those
word pairs are selected ( §4.2), and finally explain
how we estimate our identified factors of interest
over these experimental items (§4.3).
4.1 Motivation for Matching Pairs of
Declining and Stable Words
As noted earlier, frequency at time t (the start time
of our analysis) may be a powerful indicator of
which words are already in the process of decline.
En effet, we found random samples of stable words
to be on average 2–3 times more frequent than de-
clining words (with stable and declining measured
as in §4.2) at the initial time t. Initial frequency
is thus a confounding factor on which the declin-
ing and stable words need to be matched. Word
length is another potential confound we noted: Dans
addition to being highly correlated with frequency
(Zipf, 1936), word length may mask (or otherwise
interact with) the factors we have identified as
related to decline. Par exemple, shorter, more fre-
quent words tend to have more meanings as well.
While it may be of some limited interest to show
that stable words tend to be shorter than declining
words, we were interested to see the effect of
our richer lexical factors beyond this. Enfin, nous
suspect that words with different parts of speech
show different patterns of decline; donc, nous
also controlled for this potential confound.
frequency and length ranges, and with an overall
similar distribution of POS. Cependant, this ap-
proach is not sufficient, since these covariates can
interact with our factors of interest. Par exemple,
the number of meanings of words correlates with
frequency (Zipf, 1949). While there may be differ-
ences in polysemy of words at the same frequency
that are predictive of decline, when compared
over a broad range of frequencies, the differences
in numbers of meanings across that range may
swamp out differences in stable and declining
words of a particular frequency. Detecting such
differences may require complex statistical mod-
els with many parameters to capture this kind of
interaction between our factors of interest and the
confounding variables.
To address this, we take a simpler and more
controlled approach, standard in human experi-
mental work, of pairing each declining word with
a stable word with matching values on these three
covariables. C'est, for each declining word, nous
find the most stable word (above a certain stability
threshold) of the same POS, such that each pair has
a very close value of frequency and word length
(as detailed below). Because our resulting exper-
imental items are words pairs, we then perform
pairwise statistical analyses to see whether de-
clining and stable word pairs matched on these
key covariates display the predicted difference in
each of the factors we explore. (Note that con-
trolling the covariates across the declining and
stable words yields declining and stable word sets
that are not statistically independent, such that
pairwise statistical analyses are recommended.)
4.2 Selecting Declining and Stable Words
We select two sets of words, in each of English,
French, and German, to be used for testing our hy-
potheses on factors that affect lexical decline: (1)
words that gradually declined in their frequency
depuis 1800 à 2010, et (2) control words that
maintained a relatively stable frequency across
le 21 decades. The words were selected from
the Google ngrams dataset (Michel et al., 2011),
where individual years (et, par conséquent, yearly
word frequencies) were accumulated into decades,
the time unit of our analysis.
4.2.1 Identifying a Set of Declining Words
One possibility would be to ‘‘range-match’’ the
overall sets of declining and stable words on these
covariates—that is, picking words in the same
We aim for the declining set to contain words
that were in common use during the first decade
of the 19th century (1800–1810), but gradually
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have become much less common in contemporary
language.4 We define a declining word as one
exhibiting a period of gradual, steady decline (à
very low, possibly 0, frequency), followed by a
period of infrequent usage (at or near 0).
To select such words, following Stewart and
Eisenstein (2018), we define a model based on
piece-wise linear regression fitting the frequencies
of a word during the 21 decades. Officiellement, nous
find the curve of the following form that has the
least mean-squared error (MSE) to the word’s
frequency curve:
(cid:2)
X(t) =
un(b − t)
0
if t ≤ b
if t > b
where t is time in decades, and a and b are
parameters defining the curve: both a and b are
positive, and b is the value within the (0–21) range
of decades that minimizes the MSE. We thus fit
the word’s frequencies to a curve with a declining
piece (crossing the x-axis of 0 frequency at b), et
a ‘zero’ piece (horizontal at frequency 0).
We define the decline metric as the MSE be-
tween these two pieces of the fit curve and the true
frequencies. This MSE metric ensures that words
are ranked highly if they show consistent temporal
decline, followed by a period of stable usage near
0 – the target behavior for words to be considered
as having declined. We normalize the frequencies
of each word across the 21 decades because we
are interested in the relative amount of change
in that word’s frequency over time. Having ob-
served that words with higher average frequency
generally yielded higher MSE, this normalization
adjusts to put words at different frequencies on a
level playing field in calculating the MSE. (Voir
Appendix A.1 for further detail and illustration of
this normalization step.)
Words ranked highest according to the defined
metric were considered as declining candidates,
and were subject to further automatic filtering to
ensure their suitability for our analysis; for exam-
ple, we excluded words shorter than 4 characters
or whose relative frequency was less than 5×10−6
in the first decade of the 19th century, or whose
piece-wise regression crossed the x-axis within
less than 10 decades from the starting point.5
4We exclude words that underwent orthographic change,
but preserved meaning and phonetic form, from this study.
5The latter condition removed OCR errors, such as fome
for some, that are more evident in earlier decades.
Additional manual filtering was then performed
by native speakers of English, French, and Ger-
man with a linguistics background. This inspection
aimed at excluding multiple forms (par exemple., inflec-
tion) of the same word, since our predictors
are likely to have a similar effect on all words
stemming from the same lemma. We replaced
multiple variants of a word (such as German
‘ansehnliche’, ‘ansehnlich’, and ‘ansehnlichen’)
with a single representative that had the highest
frequency among them in 1800–1810 (in this case,
‘ansehnliche’).6
Our final sets of declining words comprise 300
words each for English and French, et 250 words
for German, due to the relative sparsity of the latter
in the historical part of the corpus.
4.2.2 Identifying the Matched Stable Words
As motivated in §4.1, we next select a matched sta-
ble word for each declining word in our datasets.
Spécifiquement, we match each declining word with
a stable counterpart
that maintained relatively
constant frequency over the period of 1800–2010.
The ‘stability’ criterion was measured by the
MSE of a word’s true frequencies to the hori-
zontal trend of best fit (using the same normaliza-
tion of frequencies as for declining words; voir
Appendix A.1).
The matching procedure paired each declining
word with a stable counterpart, ensuring similar-
ity in three properties that could introduce bias
into the analysis: the initial frequency of a word
(±10%), its length in characters (±2 characters,
with the additional restriction that the sum of
lengths of all stable words must be within 1 de
the sum of lengths of all declining words), et
its POS (nouns were matched with nouns, adjec-
tives with adjectives, etc.); see Appendix A.2. Pour
example, Chiffre 1 in §1 illustrates the diachronic
trends of three matched English word-pairs that
have various initial frequency, POS, and length.
The carefully curated sets of declining and stable
words facilitate rigorous analysis of the factors that
we hypothesize are predictive of lexical decline.
Spécifiquement, the matched sets enable comparison
of the factor values between pairs of words—one
stable and one declining—that are matched on
6We select a single representative word (plutôt que,
par exemple., averaging our predictors over multiple alternatives) à
facilitate pairwise word matching, since frequency, length,
and POS can differ across a set of morphologically related
words.
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key linguistic properties at the starting point of
our analysis. In this way, we control for these
matched linguistic properties, and see how differ-
ences in our identified factors correlate with the
final fate of the words—gradually experiencing
lexical decline, or soundly persisting across 210
years of language use. Appendix A.3 provides
examples of matched word-pairs for the three
languages—English, French, and German.
4.3 Estimating Factors Predictive of Decline
Here we describe how we estimate each of the 7
features we hypothesize are predictive of lexical
decline, in each of the 3 languages (cf. Tableau 1).
In each case, we calculate the feature based on
its value at the beginning of the time period we
consider (1800–1810), except as noted below.
Semantic Density (SemDens). We define se-
mantic density as the average similarity of a word
to its 10 nearest neighbors in semantic space.7 We
use the historical embeddings made available by
Hamilton et al. (2016),8 and use cosine similar-
ity between two representations in the semantic
espace. The three languages vary in availability of
these semantic representations. English benefits
from ample historical data, and all 600 words
were found. For French, 530 out of 600 word rep-
resentations were found (balanced between stable
and declining sets); we interpolated semantic den-
sity values for the missing words by using one
of the most popular data imputation methods—
assigning them the mean SemDens value of the
530 available representations. We exclude Ger-
man from analysis of this factor because only 22
of our German declining and stable words have
historical embeddings. Chiffre 2 illustrates the pre-
diction that a denser semantic neighborhood is
observed for a declining word (here ‘magnesia’,
gauche) compared to its corresponding stable word
(here ‘secrets’, droite).
Concreteness (Conc). Snefjella et al. (2019)
released a dataset of (automatically inferred) his-
torical by-decade concreteness ratings for over
20K English words, dating back to 1850. Assum-
ing that the concreteness of individual words did
7Using 20 ou 50 neighbors gave similar results; Pearson
correlations between SemDens using 10 neighbors and
SemDens using 20 ou 50 neighbors both yield r = 0.99.
8We use the word2vec (SGNS) versions (Mikolov et al.,
2013), from https://nlp.stanford.edu/projects
/histwords/.
Chiffre 2: t-SNE projection of the 10-closest neighbors
in the semantic space of the matched words ‘magnesia’
(dec):‘secrets’ (stb). The semantic neighborhood of
‘magnesia’ (gauche) is denser, compared to that of ‘secrets’
(droite). SemDens values for these words are 0.872 et
0.500, respectivement.
not undergo a significant change during the period
1800–1850, we use the scores from 1850 as a
close approximation of English concreteness rat-
ings in 1800–1810. With no access to historical
concreteness norms for French and German, nous
only calculate this feature for English.
Because only 461 out of our 600 English words
have a concreteness rating in the Snefjella et al.
(2019) dataset, we use an adaptation of the ap-
proach by Tsvetkov et al. (2013) to infer concrete-
ness values for the missing words. We train a
Beta regression model9 to predict the concreteness
scores of over 22K words in the historical data-
ensemble, from the semantic representations of the
words in the 1850s (again, using embeddings from
Hamilton et al., 2016). The full set of our 600 de-
clining/stable words was excluded from training,
as was a 1000-word held-out test set. The trained
model obtains Pearson’s correlation of 0.74 être-
tween scores inferred by our model and the actual
ratings for the 1000-word test set, as well as a
correlation of 0.78 to the ratings of the 461 rated
words in our dataset. Next we use the trained
model to predict concreteness rating of all 600 (de-
clining/stable) words.10 Among words assigned
the highest scores are ‘verdure’ and ‘diamonds’,
while their least concrete counterparts include
‘reasonings’ and ‘magnanimity’.
Number of Meanings (NMngs). We make use
of the Historical Thesaurus of English (HTE) (Kay
et coll., 2019), a database that records the meanings
9An alternative to linear regression for cases where the
dependent variable is a proportion (0–1 range).
10For consistency, we use the predicted scores for all words
in our dataset, rather than using the original ratings for those
461 words that occurred in the Snefjella et al. (2019) data.
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throughout their history for a very large number
of words. We are not aware of a resource analo-
gous to HTE for French and German, hence we
only consider this factor for English. Each dis-
tinct meaning of a word in HTE has recorded
its earliest date of use (as well as its latest date
of use, for obsolete meanings). We extracted for
each word in our English dataset the number of
unique meanings it had in 1800–1810. For exam-
ple, 10 distinct meanings were recorded for the
word ‘institution’, but only a single meaning
for ‘ignominious’. We interpolated the missing
values for 168 words not documented in HTE
(split roughly equally between declining and sta-
ble words) by assigning to them the mean number
of meanings of the 432 words documented in the
database.
Contextual Diversity (CDiv). For our distribu-
tional measure of contextual diversity, we focus
on how much the local environment of the target
word (c'est à dire., a single word before and after it) de-
viates from the distribution of words in the lan-
guage as a whole. Par exemple, consider the words
‘somewhat’ vs. ‘amok’ (part of the phrase ‘run
amok’): Because ‘somewhat’ appears in a wide
variety of linguistic contexts, the distribution of
frequencies of its immediate neighbors will be
much more similar to their distribution in the lan-
guage as a whole, compared to ‘amok’, dont
distribution over its neighbors will have a very
large peak for the word ‘run’. McDonald and
Shillcock (2001) capture this intuition by for-
mulating contextual distinctiveness (the opposite
of contextual diversity) as the Kullback-Leibler
(KL) divergence between two probability distri-
butions, the conditional distribution of words c
in the context of w, and the prior distribution of
the context words c:11
DKL(P. (c|w)||P. (c)) =
(cid:3)
P. (c|w) log
P. (c|w)
P. (c)
follows, we use DKL(w) to mean
In what
DKL(P. (c|w)||P. (c)) as defined above, with c
understood as our context words.
A higher value for DKL(w) implies that w oc-
curs in a narrow range of contextual usages—that
est, DKL is inversely related to contextual diversity.
11Dans cette étude, c ranges over the 10K most frequent
words. We exclude the top 100 words as less informative
regarding the effect of relative breadth or narrowness of
topical distribution on survivability of a word.
To obtain a measure of contextual diversity,
we scale DKL to the 0−1 range, by applying
a non-linear exponential transformation 1− exp
(–DKL), and subtract the result from 1. Officiellement,
contextual diversity of a word w at time period t
is defined as:
CDivt(w) = exp(–Dt
KL(w))
Examples of nouns with high contextual diversity
in our data are ‘money’, ’effect’, and ‘purchase’,
while words with low CDiv score include ‘pan-
egyric’, ‘soldiery’, and ‘rivulet’.
Phonological Typicality (PhonTyp). We es-
timate phonological typicality using a phoneme-
based LSTM (Hochreiter and Schmidhuber, 1997)
language model12, trained (for each language)
on the IPA transcriptions (International Phonetic
Association, 1999) of a 100K-word sample from
the Google ngrams corpus, spanning years 1800–
1810, sampled with replacement via multinomial
distribution over the word unigram frequencies
in the corpus. Word transcriptions were obtained
through Epitran (Mortensen et al., 2018), a tool
for transcribing orthographic text as IPA, and then
manually verified. We chose not to use CELEX
(Baayen et al., 1996) (which supports English
and German but not French) or a similar lexical
resource because Epitran provides broader cov-
erage and manual correction provided acceptable
accuracy. Using the trained language model, le
phonological typicality of a word is the average
log probability of the next phoneme conditioned
on the word’s prefix.
Officiellement, for a word w with length k:
PhonTyp(w) =
(cid:4)
logP (ci | c1, .., ci−1)
k
, i∈[1..k]
Phonological Density (PhonDens). Follow-
ing Bailey and Hahn (2001), we computed phono-
logical density of a word as the sum of distances
of its IPA transcription to that of all other word
types comprising the lexicon in 1800–1810. Pour-
mally, phonological density of a word w with
respect to a lexicon L is defined as:
PhonDens(w) =
(cid:3)
v∈L
exp(−d(w, v))
12With two hidden layers (75 et 50 cells), each layer
followed by batch-normalization and dropout.
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Factor
severest (D)
longest (S) solicitude (D) marriages (S) ornamented (D) attracted (S)
SemDens
Conc
NMngs
CDiv
PhonTyp
PhonDens
PhonComp
0.61
0.50
4.59
0.52
−2.93
6.02
0.60
0.41
0.77
4.59
0.95
−2.24
5.76
0.40
0.50
0.48
2.00
1.31
−3.40
5.87
0.80
0.48
0.59
4.59
1.81
−0.98
5.88
0.75
0.69
0.90
1.00
1.80
−1.62
6.03
0.40
0.51
0.78
1.00
4.40
−1.35
6.03
0.37
Tableau 2: Examples of English word-pairs with varying initial frequency, POS, and length, along with
their predictor values. ‘D’ indicates a declining word and ‘S’ a stable word. Differences in the expected
direction are boldfaced. For convenience, CDiv×103 and PhonDens×10–3 values are presented.
the distance d is
the normalized
où
Levenshtein distance (Levenshtein, 1966) être-
tween the phonetic forms of words w and v.
Phonological Complexity (PhonComp). Words
can be phonologically complex in various dimen-
sions. For ease of calculation across the three
languages in this study, we measured one of these,
the ratio of syllables to segments, by counting
the number of syllabic nuclei (vowels) et le
number of phonemes (segments). Vowels and seg-
ments in aforementioned IPA transcriptions were
classified as such according to the specifications
given by the International Phonetic Association.
A higher ratio was taken to indicate greater pho-
nological complexity, corresponding to greater
‘syllable density’.
Examples of Word Pairs and Factor Values.
Tableau 2 presents three examples of English word-
pairs along with the values computed for these
7 factors. The vast majority of differences occur
in the predicted direction, with a few exceptions
(par exemple., the higher degree of concreteness of the
declining ‘ornamented’ vs. the stable ‘attracted’).
All three declining words exhibit notably higher
SemDens, lower CDiv (extremely so for ‘or-
namented’), lower PhonTyp (extremely so for
‘solicitude’), and higher PhonComp.
5 Results and Discussion
5.1 Factor Analysis
We aim to test the predictive power of our 7 fac-
tors on a word’s likelihood to fall out of use. As a
first step, we assess the difference in the defined
predictors across the two sets of declining and
stable words in each language, by applying statis-
tical significance tests on individual factor values.
Spécifiquement, we apply the Wilcoxon pairwise
sign-ranked test on the values for each predictor,
testing whether the two (paired) samples exhibit
a significant difference in each case. Tableau 3
reports the results for the three languages, split
by factor categories—semantic, distributional, et
phonological. All our predictions (see Table 1) sont
borne out, except for PhonComp (with a signifi-
cant difference only for English) and PhonDens
(insignificant for all languages).
Chiffre 3 presents the Pearson correlations be-
tween the predictors as a heatmap (predictors
missing from French and German are left uncol-
ored). There is only one moderate correlation, de
PhonTyp with PhonDens, which is attributable
to the fact that atypically pronounced words will
tend to have fewer close phonological neighbors,
and thus sparser phonological neighborhoods.
5.2 Predicting a Word’s Future Status
Here we test whether the systematic and signifi-
cant differences among our 7 factors, as observed
in Table 3, support their use in a prediction task
regarding lexical decline. Because each declin-
ing word in our data is matched to a (control)
stable word, we use a logistic regression model
to predict the future status of the words in each
pair: which is the declining word, and which the
stable one. We examine individual regressor co-
efficients to assess the relative contribution of
individual features to the prediction task. We also
report the pseudo-r2 of the regression model, comme
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English
French
German
Factor
SemDens
Conc
NMngs
CDiv
PhonTyp
PhonDens
PhonComp
stb
0.52 (±0.07)
0.57 (±0.16)
5.26 (±4.02)
2.93 (±7.72)
dec
0.55** (±0.07)
0.53* (±0.15)
3.91** (±2.21)
1.97** (±4.10)
dec
0.65** (±0.10)
N/A
N/A
0.88** (±2.82)
stb
0.53 (±0.07)
N/A
N/A
1.20 (±3.30)
N/A
N/A
N/A
2.01 (±4.05)
−2.02* (±0.85) −1.85 (±0.71) −2.27** (±0.84) −2.00 (±0.86) −1.83** (±0.47) −1.73 (±0.46)
8.65 (±0.26)
0.44 (±0.09)
N/A
N/A
N/A
1.47** (±2.01)
(±0.12)
5.90
0.38* (±0.07)
5.92 (±0.12)
0.35 (±0.07)
5.38 (±0.12)
0.37 (±0.09)
(±0.11)
(±0.10)
(±0.27)
(±0.09)
5.37
0.38
8.65
0.45
dec
stb
Tableau 3: Mean (±SD) of factor values for declining (dec) and stable (stb) words. Significant differences
are marked by ‘**’ (p<.001) and ‘*’ (p<.01). For convenience, CDiv×103 and PhonDens×10−3
values are presented. All significant differences in factors match the direction of our prediction in
Table 1.
Figure 3: Heatmap of correlations of predictors for English (left), French (middle), and German (right). Uncolored
rows/columns denote unavailable measures in French and German. Numeric values are shown only for significant
correlations (after applying Bonferroni correction for multiple comparisons).
an indication of the collective predictive power of
our factors.13
Specifically, each item in this task is a word-pair
from our matched sets of declining (dec) and sta-
ble (stb) words (e.g., ‘thence’:‘forward’), where
the items are created such that (a random) half of
the pairs are in the order dec:stb and the other
half are in the order stb:dec. The dependent
variable in the logistic regression is a binary vari-
able indicating whether the item is in the order
dec:stb (a value of 1) or stb:dec (a value of
0). The 7 independent variables in the regression
are formed by taking the difference between the
corresponding feature values of each word in the
pair (all features scaled to the 0–1 range). As an ex-
ample, for the ‘thence’:‘forward’ word-pair, the 7
predictors are calculated by subtracting the values
of each of the 7 features of ‘forward’ from those
of ‘thence’, and the dependent variable is defined
as ‘1’, for dec:stb. We run a regression of this
form on each of the three languages; we present
detailed results on English, with comparison to
French and German.14
In English,
the logistic regression obtained
a pseudo-r2 of 0.23, while a similar analysis
for French achieved a pseudo-r2 of 0.41.15 A
pseudo-r2 of only 0.08 was obtained for German,
which has no semantic features available. Table 4
13We report here the results of a logistic regression
model, using the Python GLM Logit implementation from
https://www.statsmodels.org, with the pseudo-r2
calculation provided at https://www.statsmodels
.org/devel/discretemod.html. In Appendix A.4,
we provide the (complementary)
results of a logistic
regression-based classification task.
14We also ran a model adding features for the differences
in frequency and length for each matched word-pair; as
expected, the results were unaffected, confirming the quality
of matching on these covariates.
15The higher value for French seems due to a number of
declining scientific terms distinguished by a much higher
average SemDens, compared to stable words.
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predictor
β coeff.
std err(β)
z
p
const
0.018
CDiv
SemDens
Conc
NMngs
0.589
−0.513
−0.847
−1.491
−0.262
PhonTyp
PhonDens −0.052
PhonComp
0.218
0.135
0.154
0.150
0.204
0.472
0.158
0.150
0.149
0.137
0.891
3.825
−3.426
−4.147
−3.176
−1.661
−0.350
1.466
0.000
0.001
0.000
0.002
0.097
0.726
0.143
Table 4: Logistic regression analysis predicting
word-pair direction (1: dec:stb, or 0: stb:
dec) from pairwise differences in factor values.
Significant predictors in bold.
presents the detailed results of the model for
English. All of the semantic features (SemDens,
Conc, NMngs) and the single distributional fea-
ture (CDiv) have a significant contribution to
the model. Moreover, the sign of the β coeffi-
cient in each case matches the direction of effect
that we hypothesized, in line with the individual
factor analysis in §5.1 above. (For example, a
positive difference in SemDens is indicative of a
dec:stb word-pair, annotated with the label ‘1’
in our analysis, because SemDens values of de-
clining words tend to be higher.) On the other
hand, none of the phonological features contribute
to the model. The results on French showed a
similar pattern: SemDens was strongly predic-
tive of decline, while CDiv was marginally so.
In German, CDiv was significantly predictive, as
was PhonTyp; it isn’t clear whether phonological
form is actually more important in German, or is
simply seen to play a role when no semantic fea-
tures are available.
We conclude that semantic and distributional
features may be associated with aspects of lexical
access and learning that are strong enough to
influence word choice and consequent trends in
frequency, while phonological effects may only
‘‘fine-tune’’ word preferences that are largely
shaped by semantic need.
uniformly reduces its frequency across the entire
diversity of its contextual environments, or if it
instead gradually ‘abandons’ particular contextual
niches, thereby narrowing its linguistic dissemi-
nation. We hypothesize that declining and stable
words differ in the diachronic trend of their CDiv
values; specifically, that declining words grad-
ually fade out from certain contextual usages,
thereby reducing the number of linguistic envi-
ronments they populate (Traugott and Dasher,
2001). To corroborate this, we perform diachronic
analysis of contextual diversity. We approach
this question by using linear regression to fit a
temporal trend line over each word w’s CDiv
values, across the 21 decades – that is, regressing
t on t∈[1..21]. We expect this trend line to
CDivw
show a decreasing tendency for declining words,
indicative of contextual shrinkage, and a stable or
increasing tendency for stable words, indicative
of stability or growth of contexts. In particular,
the regression line coefficients of the declining
set should be significantly lower than that of
stable words.
However, we must adopt a multiple regression
approach that incorporates variables (other than
time) that could also contribute to variation in a
word’s CDiv values. Specifically, we identified
two properties that may bias the CDiv of a word
when comparing across decades:16 (1) the num-
ber of unique books used for data extraction in
that decade, and (2) the frequency of the word
in that decade. First, a greater number of unique
per-decade books is likely to increase contextual
diversity, since a higher number of distinct litera-
ture sources raises the chance of a wider range of
contextual domains. Second, lower frequency of
a word is likely to negatively affect its contextual
diversity—the lower the frequency, the less op-
portunity there is for a word to occur in different
contexts. We address these potential confounds by
using the per-decade values of each of these prop-
erties as additional independent variables, along
with time t, in a multiple regression.17
6 Diachronic Analysis of Lexical Loss
We next explore whether there are diachronic
patterns in the contextual dissemination of words,
over the 21 decades of our data, that differ between
declining and stable words. A specific question is
whether a word falling out of use in a language
16When using CDiv in Section 5, this was not an issue,
since we restricted the focus to a single decade, 1800–1810.
17We compute per-decade number of books by summing
the number of unique books reported in the Google-ngrams
dataset for all years of the decade. We then take the log of this
value since the relative increase in CDiv due to number of
books is likely attentuated as this number grows, motivating
the use of a sub-linear function.
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have a positive mean and median (mean = 0.0027;
median = 0.0021). Although the mean coefficient
values are small, the coefficients for the stable
words are consistently larger, as quantified by the
Wilcoxon test. Moreover, the wide range of their
(mostly positive) coefficients indicates a strong
tendency of stable words to increase in contextual
diversity and gradually occupy a broader range of
environments, contrasting with declining words.
7 Conclusions
We have proposed factors of various types—
semantic, distributional, and phonological—and
shown that the semantic and distributional features
are robust predictors of whether a word will remain
stable in frequency or fall into decline. In particu-
lar, we have focused on factors that can influence
the cognitive processing of words, affecting how
likely they are to be used and learned.
Given that broad external influences, such as
language contact, as well as social and technolog-
ical developments, are known to have a massive
effect on the content of vocabulary, this study
constitutes an important demonstration of the po-
tential influence of internal cognitive mechanisms
on the ‘survivability’ of words. Our findings sug-
gest that factors affecting a word’s trajectory are
more likely to be semantic or distributional than
phonological, perhaps because speakers or writ-
ers, when they are looking for a word, are guided
primarily by syntactic and semantic criteria.
The behavior of most of the factors we proposed
matches the expectations for declining vs. stable
words that were motivated by the psycholinguistic
literature. Our findings are consistent with an
evolutionary view where psycholinguistic factors
influence the ‘reproductive fitness’—the fitness
for self-perpetuation—of words. This, in turn,
supports a broader evolutionary research agenda
in historical linguistics.
Acknowledgments
We are grateful
to the Action Editor, Jacob
Eisenstein, and the anonymous reviewers for their
constructive and detailed feedback which helped
us improve the research. We are also thankful
to Yang Xu from the Language, Cognition, and
Computation (LCC) Group at the University of
Toronto for offering comments on an earlier ver-
sion of this work. This research was supported
Figure 4: Boxplot of the distribution of the two sets of
β3 coefficients: for declining (left) and stable (right)
words in our English dataset.
This yields the following regression model:
CDivw
t = β0 + β1 ∗ log(Bt) + β2 ∗ F w
t
+ β3 ∗ t + (cid:3)w
t
where β3, the coefficient of the decade counter
t∈[1..21], reflects the temporal trend of CDiv for
each word w in our data: the sequential tendency
of w’s contextual diversity over time, taking into
account the effects of number of books, log(Bt),
and word frequency, F w
t , in each decade t.
Our analysis now proceeds by assessing the dis-
tribution of the β3 coefficients. As noted above,
we hypothesize that these coefficients will differ
across declining and stable words; specifically,
declining words will tend to have negative β3
coefficients, indicating decreasing contextual di-
versity over time, while stable words will have
non-negative β3 coefficients, showing a flat or
increasing tendency of diversity.
Figure 4 presents two boxplots of the distribu-
tions of the two sets of β3 coefficients—for the 300
declining words (left) and 300 stable words (right)
in our English dataset.18 The means of the two
distributions significantly differ from each other,
as well as from 0, when applying a Wilcoxon test
(p<0.001 for all tests). We thus find support for
the claim that declining and stable words have
different diachronic patterns of contextual diver-
sity. The negative mean and median of the coef-
ficients for the declining words (mean = −0.0006;
median = −0.0005) further support our specific
hypothesis of diachronic contextual loss for these
words. In contrast, the coefficients of stable words
18Similar results were found for French and German.
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by NSERC grant RGPIN-2017-06506 to Suzanne
is based in part on
Stevenson. This material
research sponsored by the Air Force Research
Laboratory under agreement number FA8750-
19-2-0200. The U.S. Government is authorized
to reproduce and distribute reprints for Govern-
mental purposes notwithstanding any copyright
notation thereon. The views and conclusions con-
tained herein are those of the authors and should
not be interpreted as necessarily representing the
official policies or endorsements, either expressed
or implied, of the Air Force Research Laboratory
or the U.S. Government.
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Appendix A
A.1: Word Frequency Normalization. As
noted in §4.2, we normalize the frequencies of
each word across all decades before calculating
the fit (in MSE) of its frequency curve to the
target ‘decline’ or ‘stable’ curve. First, due to
the widely varying amounts of data available in
each decade, we take the frequency of a word as
its relative frequency within each decade. Further
normalization was motivated by our observation
that words at different frequency levels could have
very different MSE values with respect to the fit
curves, with higher frequencies generally leading
to higher MSEs.
An example is illustrated in the left panel of
Figure 5, which shows the per-decade relative fre-
quencies of two declining words – ‘thence’ and
‘verdure’ – with their corresponding (declining)
fit line. (Recall that we take the MSE between a
piecewise curve with a declining piece and a ‘zero’
piece that is horizontal at 0; the lines shown in
Figure 5 are the declining pieces.) The higher ini-
tial frequency of ‘thence’ potentially contributes
to higher MSE: A 10% offset from the fit line
contributes more to MSE of ‘thence’ than of ‘ver-
dure’. Normalization of each word’s frequencies
(dividing by its total frequency across the decades)
eliminates this confound by yielding a curve that
reflects relative change across the decades, as
exemplified in the right panel of Figure 5.
A.2: Finding Matched Stable Words. The
matching procedure greedily matches each de-
clining word with the first stable counterpart that
meets all three constraints (on initial frequency,
length, and POS) by traversing the list of stable
words sorted by their ‘stability’ measure, so as to
exploit the most stable words first. The matched
stable word is then removed from the stable list, so
that it will not be considered for further matches.
The Wilcoxon pairwise sign-ranked test on the
frequency and length of the matched word-pairs
revealed no significant differences, implying that
no bias was introduced into the selection process
with respect to the control factors.
A.3: Example Declining–Stable Pairs. Table 5
presents 10 sample word-pairs for English, French,
and German. Recall that words are matched by
initial frequency (±10%), length in characters (±2
characters, with the additional restriction that the
Figure 5: Per-decade frequencies of two declining
words and their corresponding fit lines: raw frequencies
(left) and normalized frequencies (right).
sum of lengths of all stable words must be within 1
of the sum of lengths of all declining words), and
POS (nouns were matched with nouns, adjectives
with adjectives, etc.).
A.4: Predicting a Word’s Future Status in a
Classification Task. The regression analysis in
Section 5.2 can alternatively be formulated as a
classification task distinguishing the declining and
stable word of a pair. Each classification item is
a word-pair from our matched sets of declining
(dec) and stable (stb) words (e.g., ‘thence’:
‘forward’), concatenating the n features extracted
for each of the two words into a single feature
vector of 2n values, where the first half represents
the first word in the pair (e.g., ‘thence’) and the
second half, the second word (e.g., ‘forward’). The
items are created such that (a random) half of the
pairs are in the order dec:stb and the other half
stb:dec, with the appropriate training label; for
a test item, the classifier must output dec:stb or
stb:dec. Due to the relatively small dataset, we
use a leave-one-out evaluation paradigm. Average
classification accuracy higher than random (0.5)
will be indicative of the predictive power of our
identified factors.
Using the classifier version of logistic regres-
sion,19 we obtain a classification accuracy of 0.67,
0.80, and 0.61 for English, French, and German,
respectively. Recall that French has many de-
clining medical terms with a higher SemDens,
leading to an easier classification task, while Ger-
man has no semantic features available, which
were shown to be highly predictive of decline in
19https://scikit-learn.org/stable/modules
/generated/sklearn.linear model.LogisticRegression
.html.
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English
French
German
dec
stb
dec
stb
dec
stb
verdure
impracticable
unexampled
dignities
insensibility
amusements
illustrious
necessaries
sublimity
whence
criminals
unreasonable
invaluable
extinction
embarrassment
foundations
successful
repetition
attainment
highly
industrieux
´evacuations
estimable
intr´epidit´e
factieux
mˆachoire
magn´esie
r´efraction
sulfurique
prairial
l´egislative
inventions
acquises
irr´egularit´e
habituel
surprise
d´esert
conversion
naturelles
arbitraire
tugendhaft
d¨unkt
endigen
hernach
mannigfaltige
f¨uglich
siebenten
redlichen
erstlich
dermalen
schwarzer
h¨angen
brauche
innen
gegenseitigen
dringend
t¨agliche
einseitigen
einziges
halbes
Table 5: Examples of declining–stable word pairs for English, French and German, selected
according to the policy described in Section 4.2, further detailed in Appendix A.
the other languages. Although the accuracy for
English is not high, it is well above random, and it
must be remembered that we are only testing our
cognitive features, and not including the myriad
social and cultural influences on lexical change.
The results here further support our findings
in Section 5.2, indicating again that the features
we have proposed have useful predictive power in
identifying the declining word of a pair that shares
similar frequency, length, and POS.
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