Modeling the Role of Polysemy in Verb Categorization

Elizabeth Soper & Jean-Pierre Koenig*

Abstract. Recent work has indicated that static word embeddings can predict human
semantic categories (Majewska et al. 2021). In this paper, we consider the role of
polysemy in semantic categorization, by comparing sense-level embeddings with pre-
viously studied static embeddings in their prediction of human-produced categories.
We find that the polysemy is crucial for predicting human categories; sense-level em-
beddings dramatically outperform static embeddings in predicting semantic categories.
Our findings highlight the role of polysemy in semantic categorization that is exclu-
sively based on linguistic input.
Keywords. Distributional semantics; polysemy; categorization; natural language pro-
cessing.

1. Introduction. As we learn language and learn about the world, we acquire semantic knowl-
edge. This knowledge comes both from perceptual input (what we experience first-hand) as well
as linguistic input (what we hear and read about). We organize our knowledge of categories, and
the words we use to denote them, based on connections we form between words and the things
they denote in the world, as well as connections between words and other words. In this paper, we
are interested in exploring two interrelated issues: (1) how much of human categories may come
from what we hear or read? and (2) whether partitioning word representations into similar con-
texts of use (a proxy for fine-grained polysemy) improves the fit of linguistic knowledge to human
categories?

Distributional semantic models are well-suited to help us answer these two questions as they
use linguistic co-occurrence statistics from a corpus to create vector representations of word mean-
ings. Words with similar meanings, which occur in similar contexts, end up near each other in
space, while unrelated words, which occur in very different contexts, end up far apart in space.
Word embeddings have become popular in recent years, in particular for their ability to accurately
predict the similarity between words (Landauer & Dumais 1997, Pereira et al. 2016, Devlin et al.
2019). Since similarity is a primary criterion for categorization (Collins & Loftus 1975), word
embeddings may also be good at predicting semantic categories.

Because word embeddings are trained on linguistic input only, with none of the perceptual
input that humans receive, they are ideal for studying the unique role of language in semantic
categorization. Additionally, because current word embeddings keep track of the contexts in which
words are found, we can look specifically at how polysemy affects how semantic categorization can
derive from linguistic knowledge. As words generally have multiple possible senses, categorization
decisions may depend on which which sense of a word is being considered. Representing the
distinct senses of polysemous words is thus likely to be important to how humans categorize sets
of verbs’ denotations and how these categories to denotations are used to approximate categories

*The authors gratefully acknowledge the audience at the Experiments in Linguistic Meaning conference, hosted by
the University of Pennsylvania, for their useful comments and feedback. Authors: Elizabeth Soper, SUNY at Buffalo
(esoper@buffalo.edu) & Jean-Pierre Koenig, SUNY at Buffalo (jpkoenig@buffalo.edu).

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c©2023 Elizabeth Soper and Jean-Pierre Koenig
Published by the LSA with permission of the author(s) under a CC BY license. 278

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of objects. We compare different types of word embeddings when it comes to predicting human
categories, and find that representing individual senses is indeed crucial for predicting semantic
categories.

2. Background. There are two main classes of word embedding models: traditional static embed-
dings (Landauer & Dumais 1997, Mikolov et al. 2013, Pennington et al. 2014) represent each word
type as a unique vector, while more recent contextual models (Peters et al. 2018, Devlin et al. 2019)
generate a unique representation for every instance of a word in context. Since both static and con-
textual embeddings have been shown to model pairwise similarity between words well (Pereira
et al. 2016, Chronis & Erk 2020), and since similarity is a primary criterion for categorization,
it seems intuitive that word embeddings should predict categorization well. Some previous work
supports this intuition; word embeddings have excelled at word sense disambiguation (Giulianelli
et al. 2020, Soler & Apidianaki 2021, Chronis & Erk 2020) and topic modeling (Sia et al. 2020,
Aharoni & Goldberg 2020), when cast as categorization problems.

As mentioned in the introduction, we are interested in the present paper in linguistically-based
semantic category induction. Instead of grouping individual word tokens into distinct senses, or
documents into topics, the goal of semantic categorization is to group unique words into semanti-
cally related clusters. This more abstract type of categorization has received less attention in the
word embedding literature.

In order to understand what word embeddings can tell us about the role of polysemy in deriving
semantic categories from linguistic input, it is important to note that there are at least two different
approaches to explaining polysemy. One account holds that polysemous words have a single,
under-specified meaning, and that context helps disambiguate between a defined set of senses
(Pustejovsky 1998). Static models are analogous to this view of meaning, as they create a single
representation which is meant to encompass all uses of a word form. A stronger claim has been
made (for example, by Elman 2009; see also Marvel & Koenig 2015) that different senses of a
word are not simply reflected in, but actually created by context. Proponents of this claim believe
that word meaning is fundamentally context-dependent. Contextual language models like BERT
implicitly take this view of word meaning, as they represent each instance of a word in a particular
context as a unique embedding. We can then treat classes of contexts as equivalent to senses in
these models.

Because static and contextual models align with different theoretical perspectives on poly-
semy, word embeddings are well-suited to test these two conceptions of polysemy and their effect
on linguistically-based category induction; static embeddings are a proxy for the single-entry view,
and contextual embeddings for the radically context-dependent view. Previous work using word
embeddings to model semantic categorization has treated semantic categorization as categorization
of word types, rather than word senses. By using static embeddings only, it implicitly assumed that
people use a summary representation to categorize words. While summary or underspecified rep-
resentations of word meanings is appropriate for many tasks, as Pustejovsky (1998) shows, much
work in psycholinguistics suggests that when reading words in and out of context, native speakers
often favor one sense or the other (see, among others, Brocher et al. 2018 for evidence and sum-
mary). In using linguistic input to derive (part of) their semantic knowledge, learners therefore
most likely rely on the word sense instantiated in the input. As a result, categorization decisions

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may depend on which sense of a word is being considered and any attempt to determine how much
semantic knowledge may derive from linguistic input would be best served by taking polysemy
into consideration. If this is the case, we would expect contextual embeddings to predict semantic
categories better than static embeddings.

Interestingly, recent work evaluating different word embedding models on verb categorization
suggests just the opposite. Majewska et al. (2021) found that contextual models perform poorly
compared to older static models when approximating the verb categorization done by participants
in their experiments. We argue that this result is due not to the irrelevance of context to cate-
gorization, but rather to the way the contextual embeddings were extracted from the model in
Majewska et al. (2021). Although many of the words in Majewska et al. (2021)’s ground truth
data are polysemous and are assigned to multiple categories by participants, they evaluate mod-
els in a one-representation-per-word-form manner. Even when evaluating BERT, which has been
shown to encode sense-specific information (Chronis & Erk 2020, Soler & Apidianaki 2021), this
information was thrown away, either by feeding words to the model in isolation or by averaging
over all contexts. Because they use polysemous data to test representations which do not encode
sense information, Majewska et al. (2021)’s results may not reflect the full potential of contextual
architectures to model categorization.

In this study, we test the ability of static and sense-specific embeddings to predict human-
produced categories. Our ultimate goal is to determine the role linguistic input plays in semantic
knowledge; our more modest goal in this paper is to find out whether polysemy matters in modeling
verb categorization. Our overall finding is that, as we predicted, sense-specific embeddings are
much better at predicting human behavior than static embeddings.

3. Dataset. We use the Phase 1 data from SpA-Verb (Majewska et al. 2021), which contains
825 verbs sorted into 17 broad classes. 10 participants were asked to sort words by clicking and
dragging each word into a circle (see Figure 1). There were no constraints on the number or size
of groups participants could create; they were only asked to sort them according to their meaning.
Table 1 gives an overview of the classes resulting from this sorting task. 116 verbs belong to more
than one class. No words were assigned to more than 3 classes. On average, words are assigned to
1.14 classes.

The SpA-Verb dataset is particularly relevant for our goals because the categories reflect actual
human behavior, rather than expert-curated categories. Additionally, since words were presented in
isolation with no disambiguating context, using this dataset is a strong test of the role of polysemy.
If people categorize according to individual senses rather than a summary representation of each
word, even when shown words out of context, this would support a context-sensitive view of
meaning and suggest that polysemy matters when building semantic knowledge from linguistic
input.

4. Models. Next we describe the models we compared against our baseline of human perfor-
mance.

4.1. WORD2VEC. The first model we evaluate is a word2vec model trained on part-of-speech-
tagged data (Fares et al. 2017). Part-of-speech tagging allows the static model to distinguish
between senses which have different parts of speech (e.g. duck NOUN and duck VERB), al-

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Figure 1: Screen interface for the SpA-Verb sorting task (Majewska et al. 2021)

Cluster label Example verbs
movement wander, fly, glide, roam
communication persuade, command, tell
crime & law beat, abduct, abuse, shoot
negative emotion offend, aggravate, enrage
positive emotion admire, respect, adore, like
cognitive process suppose, assume, realize
cooking cook, slice, stew, boil
possession belong, obtain, acquire

Table 1: A sample of the 17 gold classes in SpA-Verb dataset (labels are given for descriptive
purposes only)

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though senses which have the same part of speech are still conflated into a single vector (e.g.
get#ACQUIRE and get#UNDERSTA-ND). Skip-gram with negative sampling was used to train the
model on Gigaword 5th Edition (Parker et al. 2011), with a context window size of 5 and 300
dimensions. Six words from SpA-Verb (do, have, be, own, slurp, and snooze) were not in the
model’s vocabulary (due either to removal of stop words from corpus or rare words not occurring
in corpus) and were thus excluded from our analysis.

4.2. BERT. We evaluate three methods of extracting embeddings from the contextual BERT
model: two baseline methods, which create one representation per word form, and a multi-prototype
method which generates one representation per word sense. For all methods, we use BERT Base
Uncased from HuggingFace’s transformers package (Wolf et al. 2020).

4.2.1. DECONTEXTUALIZED (DECONT). First and most simply, we extract embeddings from
BERT by feeding each word to the model in isolation. This creates a single, static embedding
for each word. This strategy has been used previously as a way to easily extract ‘context-free’
representations from BERT (Liu et al. 2019, Vulić et al. 2020).

4.2.2. AGGREGATED (AGGR). Next, we create static embeddings from BERT by averaging a
word’s embeddings across 100 unique contexts. This aggregated approach still reduces a word
to a single representation, but has been shown to produce higher quality representations than the
decontextualized strategy (Bommasani et al. 2020).

4.2.3. MULTIPROTOTYPE (MPRO). Finally, to test whether sense-specific information is impor-
tant to semantic categorization, we distill token-level BERT embeddings into multiple prototype
embeddings. We use the method of Chronis & Erk (2020) to generate representations which cor-
respond to different senses of a word, without collapsing every token into a single representation.
Multi-prototype embeddings were generated as follows:

1. For each verb in the dataset, we sampled up to 100 sentences from the British National
Corpus (BNC Consortium 2007), excluding non-verbal uses of the target word. A few words
in the set occurred in the corpus fewer than 100 times. Four words (broil, corrupt, exhale,
and misspend) did not occur as verbs at all in the corpus and were excluded from our analysis.
The average number of occurrences sampled for a word was 95.6.

2. We extract BERT token embeddings for each collected occurrence of a word. For words
which BERT tokenizes into multiple word pieces, we average over all component pieces.

3. We cluster the token embeddings for each verb. Like Chronis & Erk (2020), we use k-means
clustering to group tokens into ‘sense’ clusters. We use the number of verb senses listed in
WordNet (Miller 1995) to determine the appropriate k for each word. Verbs in the dataset
had on average 5.9 senses. (min: 1, max: 59, for buzz).

4. After identifying clusters, we take the k cluster centroids for each word. These are the
embeddings we evaluate against the SpA-Verb categorization data.

4.3. RANDOM BASELINE. Finally, we generate random vectors and evaluate them in order to
establish a baseline for random chance performance.

5. Evaluation. To evaluate the performance of each model against a ‘gold standard’ set of human-
generated categories, k-means clustering is used to group verbs into predicted classes. We use the

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same metrics as Majewska et al. (2021): modified purity and weighted class accuracy are combined
in an F1 score, calculated as their balanced harmonic mean. Modified purity is the mean precision
of predicted clusters:

MPUR =

∑
C∈Clust,nprev(C)>1nprev(C)

#test verbs
(1)

where each cluster C from the set of all KClust induced clusters Clust is associated with its preva-
lent gold class, and nprev(C) is the number of verbs in an induced cluster C taking that prevalent
class, with all other verbs considered errors. #test verbs is the total number of verbs in the dataset.
While modified purity is a measure of precision, weighted class accuracy targets recall:

WACC =

∑
C∈Goldndom(C)

#test verbs
(2)

where for each class C from the set of gold standard classes Gold, we identify the dominant cluster
from the set of induced clusters having most verbs in common with C (ndom(C)).

Because MPro BERT has multiple representations for a single word, the same word form
may show up more than once within a single cluster. To prevent artificially inflating the recall in
evaluating MPro BERT, we eliminate duplicates within each cluster before evaluation.

6. Results. Table 2 shows the results of each embedding type, compared to results reported in
Majewska et al. (2021). The baseline models (Decont. and Aggr. BERT) perform comparably to
previously reported results. Part-of-speech-sensitive word2vec model scores about 10 points higher
than reported for a similar model architecture without part-of-speech information. MPro BERT,
by contrast, performs dramatically better than other embeddings, achieving more than double the
F1 score of the best previously reported BERT results. This suggests that polysemy does play an
important role in modeling linguistically-based semantic categorization: human categories that can
be derived from linguistic input correspond to semantically similar contexts of use of words.

Model F1-optimal F1-gold
Random baseline 0.204 0.161

Majewska word2vec 0.355 0.326
Majewska best BERT 0.340 0.322
POS-tagged word2vec 0.442 0.433

Decont. BERT 0.309 0.191
Aggr. BERT 0.398 0.346
MPro BERT 0.743 0.687

Table 2: Average F1 across models on coarse-grained categories. ‘Gold’ is for k=17, as in the
ground truth. ‘Optimal’ is best result for k in the range (5, 50).

The benefit of sense-specific embeddings for modeling coarse-grained categoirzation is clear
in the example of freeze. In the ground truth data, freeze belongs to just one class, related to cooking
(along with words like bake, fry, melt, and thaw). Freeze has another figurative sense, meaning to
stop or suspend. Because the word is polysemous, static embedding clusters struggle to categorize

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it appropriately. In the aggregated BERT clusters, freeze appears in a cluster predominated by
verbs related to violence (whip, shoot, choke, crush, smash). Decontextualized BERT puts freeze
in a heterogeneous cluster with a few cooking words (melt, stew, fry) but also many seemingly
unrelated words (knit, greet, disturb, wander). It appears that the different senses of the word skew
its static representation and prevent accurate classification. MPro BERT, by contrast, puts freeze
in two clusters: one related to cooking (as in the ground truth) and another cluster with words
like stop, delay, arrest and restrict, which seems to correspond to the figurative sense of freeze.
Thus factoring out different senses allows MPro BERT to give a more accurate and reasonable
categorization.

7. Discussion. One might argue that the superior performance of MPro BERT embeddings is due
simply to the increased total number of embeddings for this setting, compared to the static em-
beddings; because MPro BERT has multiple representations per word form, it has more ‘chances’
to correctly categorize each word. We do find that the MPro BERT clusters have more members
on average: the average ground truth class has 55.5 members, while for our optimal MPro BERT
results, the average cluster size was 96.0 words. On average, one word form appeared in 3.02
clusters, but only in 1.14 ground truth classes. All else being equal, the larger cluster size and the
fact that words appear in more clusters should lead to higher recall scores and lower precision. In
fact, we find that MPro BERT embeddings have higher precision and higher recall scores than the
static embeddings, confirming that the difference we see between MPro BERT and static embed-
dings is not merely a fluke; sense-level embeddings really do seem to better capture broad semantic
categories better.

In fact, MPro BERT might be better at modeling human participants than suggested by our F1
measure. This is because MPro BERT tends to capture more distinct senses per word than human
participants did, as human participants generally focused on a single sense when categorizing.
For example, the word form jump occurs in one MPro BERT cluster corresponding to violence
(jump#ATTACK), another cluster corresponding to physical movement (jump#HOP), and a third
one related to change (jump#INCREASE). In the ground truth data, jump only occurs once, in a
class related to physical movement. Perhaps this is the most salient sense of the word jump, and
therefore participants were more likely to be thinking of this sense during the word sorting task and
ignore its other possible senses. Our F1 score is therefore a conservative measure of the success of
MPro BERT in modeling human categorization exclusively from linguistic input, in that it counts
these other non-dominant senses of jump against MPro BERT in our evaluation. However, the
fact that embeddings for jump were assigned three separate clusters is not necessarily a weakness:
the MPro BERT clusters are more thorough as they represent each sense of the word separately
and appropriately assign them to separate clusters. MPro BERT clusters correspond to all possible
ways of classifying sets of contexts (sense) words appear in to derive semantic categories; human
participants seem to be more likely to grab onto fewer or even only one of set of contexts (sense).

As the previous example suggests, our conservative F1 scores may not give a full picture of
the quality or reasonableness of the word embedding clusters, as it abstracts away from the fact
that human participants might only attend to a few or even one sense of words they semantically
categorize even when those words are presented in isolation. Our current results also abstract
away from an important aspect of categorization, namely its flexibility and context-dependency:

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categorization is a relatively flexible task. There may be many possible criteria for sorting a group
of words, especially when given such a large set of words to sort (Tversky 1977, Barsalou 1982).
The flexibility of human categorization might explain the low inter-annotator agreement on the
task; Majewska et al. (2021) measured the agreement between two initial test participants on the
verb sorting task, which was just 0.400 B-Cubed score. This low agreement suggests that humans
don’t perform very consistently in creating broad semantic categories from a large group of words.
Even within a single participant, categories were not created based on consistent criteria. As
a result, it’s possible for induced categories from word embeddings to be reasonable, but still
correlate poorly with our ground truth data. To fully determine how much semantic knowledge
can be extracted from linguistic input, it is critical for models based on word embeddings to be
able to mimic the flexibility and contextual-dependence of human categorization.

8. Conclusion and Future Work. Majewska et al. (2021) found that static word2vec embeddings
were better at predicting human categorization of verbs than contextual BERT embeddings. In this
paper, we challenged this result, comparing sense-specific embeddings against previously evalu-
ated static representations, and found that the rich, sense-specific information present in BERT
allows it to excel at predicting semantic categories. On a more general level, these results suggest
that linguistic input encodes a great deal of information about semantic categories, independently
of other perceptual input that humans receive, and that this category information can be extracted
from embedding models which are trained on linguistic data alone. These results also suggest
that word meanings that derive from linguistic input are better able to model human categorization
if they correspond to semantically similar sets of contexts (the distributional equivalent of word
senses). More broadly, our research supports the words-as-cues theory put forth in Elman (2009)
in that it is similar sets of contexts of use that best predict human categorization. Future work
is needed, though, to extend this analysis to nouns, which may behave differently, as well as to
further explore the role of language in forming semantic categories, and whether models like those
discussed here can model the flexible, goal-dependent nature of human categorization.

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