In *Proceedings of the 13th EURALEX International Congress*, pages 425–432, Barcelona, Spain. Institut Universitari de Linguistica Aplicada, Universitat Pompeu Fabra.

Cunliang Kong, Yujie Wang, Ruining Chong, Liner Yang, Hengyuan Zhang, Erhong Yang, and Yaping Huang. 2022. Blcu-icall at semeval-2022 task 1: Cross-attention multitasking framework for definition modeling. *arXiv preprint arXiv:2204.07701*.

Alon Lavie and Abhaya Agarwal. 2007. Meteor: An automatic metric for mt evaluation with high levels of correlation with human judgments. pages 228–231.

Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension.

Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In *Text Summarization Branches Out*, pages 74–81, Barcelona, Spain. Association for Computational Linguistics.

Timothee Mickus, Denis Paperno, and Matthieu Constant. 2019. Mark my word: A sequence-to-sequence approach to definition modeling. In *Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing*, pages 1–11, Turku, Finland. Linköping University Electronic Press.

George Miller. 1995. Wordnet: A lexical database for english. *Communications of the ACM*, 38:39–.

D.I. Moldovan and Rada Mihalcea. 2000. Using wordnet and lexical operators to improve internet searches. *Internet Computing*, IEEE, 4:34 – 43.

Jorge Morato, Miguel Marzal, Juan Llorens, and Jos Moreiro. 2004. Wordnet applications. *Proceedings of the 2nd Global Wordnet Conference*, 2004.

Nikola Mrkšić, Ivan Vulić, Diarmuid O Séaghdha, Ira Leviant, Roi Reichart, Milica Gašić, Anna Korhonen, and Steve Young. 2017. Semantic specialization of distributional word vector spaces using monolingual and cross-lingual constraints. *Transactions of the association for Computational Linguistics*, 5:309–324.

Tuan-Phong Nguyen, Simon Razniewski, Julien Romero, and Gerhard Weikum. 2022. Refined commonsense knowledge from large-scale web contents. *IEEE Transactions on Knowledge and Data Engineering*, pages 1–16.

Ke Ni and William Yang Wang. 2017. Learning to explain non-standard English words and phrases. In *Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*, pages 413–417, Taipei, Taiwan. Asian Federation of Natural Language Processing.

Thanapon Noraset, Chen Liang, Larry Birnbaum, and Doug Downey. 2017. Definition modeling: Learning to define word embeddings in natural language. pages 3259–3266.

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: A method for automatic evaluation of machine translation. In *Proceedings of the 40th Annual Meeting on Association for Computational Linguistics*, ACL ’02, page 311–318, USA. Association for Computational Linguistics.

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language Models are Unsupervised Multitask Learners.

Vered Shwartz, Yoav Goldberg, and Ido Dagan. 2016. Improving hypernymy detection with an integrated path-based and distributional method. *arXiv preprint arXiv:1603.06076*.

Ivan Vulić, Daniela Gerz, Douwe Kiela, Felix Hill, and Anna Korhonen. 2017. Hyperlex: A large-scale evaluation of graded lexical entailment. *Computational Linguistics*, 43(4):781–835.

Ivan Vulić and Nikola Mrkšić. 2017. Specialising word vectors for lexical entailment. *arXiv preprint arXiv:1710.06371*.

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierre Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2020. Transformers: State-of-the-art natural language processing. In *Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations*, pages 38–45.

Ruimin Zhu, Thanapon Noraset, Alisa Liu, Wenxin Jiang, and Doug Downey. 2019. Multi-sense definition modeling using word sense decompositions.

Michael Zock, Olivier Ferret, and Didier Schwab. 2010. Deliberate word access: an intuition, a roadmap and some preliminary empirical results. *International Journal of Speech Technology*, 13(4):201–218.
In *Proceedings of the 13th EURALEX International Congress*, pages 425–432, Barcelona, Spain. Institut Universitari de Linguistica Aplicada, Universitat Pompeu Fabra.

Cunliang Kong, Yujie Wang, Ruining Chong, Liner Yang, Hengyuan Zhang, Erhong Yang, and Yaping Huang. 2022. Blcu-icall at semeval-2022 task 1: Cross-attention multitasking framework for definition modeling. *arXiv preprint arXiv:2204.07701*.

Alon Lavie and Abhaya Agarwal. 2007. Meteor: An automatic metric for mt evaluation with high levels of correlation with human judgments. pages 228–231.

Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension.

Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In *Text Summarization Branches Out*, pages 74–81, Barcelona, Spain. Association for Computational Linguistics.

Timothee Mickus, Denis Paperno, and Matthieu Constant. 2019. Mark my word: A sequence-to-sequence approach to definition modeling. In *Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing*, pages 1–11, Turku, Finland. Linköping University Electronic Press.

George Miller. 1995. Wordnet: A lexical database for english. *Communications of the ACM*, 38:39–.

D.I. Moldovan and Rada Mihalcea. 2000. Using wordnet and lexical operators to improve internet searches. *Internet Computing*, IEEE, 4:34 – 43.

Jorge Morato, Miguel Marzal, Juan Llorens, and Jos Moreiro. 2004. Wordnet applications. *Proceedings of the 2nd Global Wordnet Conference*, 2004.

Nikola Mrkšić, Ivan Vulić, Diarmuid O Séaghdha, Ira Leviant, Roi Reichart, Milica Gašić, Anna Korhonen, and Steve Young. 2017. Semantic specialization of distributional word vector spaces using monolingual and cross-lingual constraints. *Transactions of the association for Computational Linguistics*, 5:309–324.

Tuan-Phong Nguyen, Simon Razniewski, Julien Romero, and Gerhard Weikum. 2022. Refined commonsense knowledge from large-scale web contents. *IEEE Transactions on Knowledge and Data Engineering*, pages 1–16.

Ke Ni and William Yang Wang. 2017. Learning to explain non-standard English words and phrases. In *Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*, pages 413–417, Taipei, Taiwan. Asian Federation of Natural Language Processing.

Thanapon Noraset, Chen Liang, Larry Birnbaum, and Doug Downey. 2017. Definition modeling: Learning to define word embeddings in natural language. pages 3259–3266.

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: A method for automatic evaluation of machine translation. In *Proceedings of the 40th Annual Meeting on Association for Computational Linguistics*, ACL ’02, page 311–318, USA. Association for Computational Linguistics.

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language Models are Unsupervised Multitask Learners.

Vered Shwartz, Yoav Goldberg, and Ido Dagan. 2016. Improving hypernymy detection with an integrated path-based and distributional method. *arXiv preprint arXiv:1603.06076*.

Ivan Vulić, Daniela Gerz, Douwe Kiela, Felix Hill, and Anna Korhonen. 2017. Hyperlex: A large-scale evaluation of graded lexical entailment. *Computational Linguistics*, 43(4):781–835.

Ivan Vulić and Nikola Mrkšić. 2017. Specialising word vectors for lexical entailment. *arXiv preprint arXiv:1710.06371*.

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierre Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2020. Transformers: State-of-the-art natural language processing. In *Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations*, pages 38–45.

Ruimin Zhu, Thanapon Noraset, Alisa Liu, Wenxin Jiang, and Doug Downey. 2019. Multi-sense definition modeling using word sense decompositions.

Michael Zock, Olivier Ferret, and Didier Schwab. 2010. Deliberate word access: an intuition, a roadmap and some preliminary empirical results. *International Journal of Speech Technology*, 13(4):201–218.
# Exploring Nominal Coercion in Semantic Spaces
with Static and Contextualized Word Embeddings

Chenxin Liu and Emmanuele Chersoni

The Hong Kong Polytechnic University
Department of Chinese and Bilingual Studies

Yuk Choi Road 11, Hung Hom, Kowloon, Hong Kong (China)

chenxin.liu@connect.polyu.edu.hk, emmanuelechersoni@gmail.com

## Abstract

The distinction between *mass* nouns and *count* nouns has a long history in formal semantics, and linguists have been trying to identify the semantic properties defining the two classes. However, they also recognized that both can undergo meaning shifts and be used in contexts of a different type, via *nominal coercion*.

In this paper, we present an approach to measure the meaning shift in count-mass coercion in English that makes use of static and contextualized word embedding distance.

Our results show that the coercion shifts are detected only by a small subset of the traditional word embedding models, and that the shifts detected by the contextualized embedding of BERT are more pronounced for mass nouns.

## 1 Introduction

The literature in formal semantics has debated for long on the distinction between *count* nouns and *mass* nouns, which has often been described as an opposition between discrete, countable objects and substances that cannot instead be divided into subunits. A notorious formal characterization of this intuition is provided by Link (1983): mass nouns like *wine* are *non-quantized*, in the sense that each subpart of *wine* will still count as *wine*; on the other hand, count nouns like *cat* are *quantized*, because if you take a subpart of a *cat*, it will not count as a *cat* (Cheng, 1973). According to such view, in other words, the two types of nouns denote in different domains with different properties.

Chomsky (1965) proposed instead a lexicalist perspective on the problem, where nouns are marked with a binary feature ± COUNT determining the kind of syntactic context (mass or count) in which they can appear. Although the approaches adopt different criteria for defining the "countability" of the nouns, they both predict that count nouns will (mostly) appear in count contexts, and mass nouns will (mostly) appear in mass contexts.

However, cases like the following are extremely frequent in natural language:

1. There is *rabbit* in my soup. (count to mass)

2. Two *wines* at table four! (mass to count)

In 1., the count noun *rabbit* is interpreted as *rabbit meat*, while in 2. the plural form of the mass noun *wine* means *glasses of wine*. Both cases are examples of **coercion**, a semantic phenomenon occurring when the standard interpretation of an expression (in our case, the noun) yields an impossible conceptual representation (e.g. in 1. a rabbit swimming in the soup) (Wiese and Maling, 2005); consequently, a more plausible interpretation is retrieved by "enriching" the semantic representation with concepts that are associated to the standard interpretation of the target expression (*enriched composition*; see Jackendoff (1997)). The focus of this paper is specifically on nominal coercion of mass and count nouns.

Since it is rare to find nouns that occur exclusively in either mass or count contexts, it makes more sense to talk about predominantly count and predominantly mass nouns. Chierchia (2010) describes the idea of mass-count *elasticity*, meaning that any noun can be in principle mass or count, its status being determined at the level of the nominal phrase. When we say "predominantly" mass or count noun, therefore, we mean that a noun has the tendency to occur more frequently in one of the two context types. On such basis, the count-mass distinction can be intuitively seen as a continuum, with the nouns traditionally described in the literature being closer to one the two extremes (Katz and Zamparelli, 2012).

In this work, we investigate to what extent modern *Distributional Semantic Models* -which are nowadays the standard for the representation of lexical meaning in NLP- encode the meaning shifts caused by mass-count coercion. We run two different experiments, making use respectively of
Exploring Nominal Coercion in Semantic Spaces
with Static and Contextualized Word Embeddings

Chenxin Liu and Emmanuele Chersoni

The Hong Kong Polytechnic University
Department of Chinese and Bilingual Studies

Yuk Choi Road 11, Hung Hom, Kowloon, Hong Kong (China)

chenxin.liu@connect.polyu.edu.hk, emmanuelechersoni@gmail.com

**Abstract**

The distinction between *mass* nouns and *count* nouns has a long history in formal semantics, and linguists have been trying to identify the semantic properties defining the two classes. However, they also recognized that both can undergo meaning shifts and be used in contexts of a different type, via *nominal coercion*.

In this paper, we present an approach to measure the meaning shift in count-mass coercion in English that makes use of static and contextualized word embedding distance.

Our results show that the coercion shifts are detected only by a small subset of the traditional word embedding models, and that the shifts detected by the contextualized embedding of BERT are more pronounced for mass nouns.

# 1 Introduction

The literature in formal semantics has debated for long on the distinction between *count* nouns and *mass* nouns, which has often been described as an opposition between discrete, countable objects and substances that cannot instead be divided into subunits. A notorious formal characterization of this intuition is provided by Link (1983): mass nouns like *wine* are *non-quantized*, in the sense that each subpart of *wine* will still count as *wine*; on the other hand, count nouns like *cat* are *quantized*, because if you take a subpart of a *cat*, it will not count as a *cat* (Cheng, 1973). According to such view, in other words, the two types of nouns denote in different domains with different properties.

Chomsky (1965) proposed instead a lexical-
ist perspective on the problem, where nouns are
marked with a binary feature ± COUNT determin-
ing the kind of syntactic context (mass or count) in
which they can appear. Although the approaches
adopt different criteria for defining the "countabil-
ity" of the nouns, they both predict that count nouns
will (mostly) appear in count contexts, and mass
nouns will (mostly) appear in mass contexts.

However, cases like the following are extremely
frequent in natural language:

1. There is *rabbit* in my soup. (count to mass)

2. Two wines at table four! (mass to count)

In 1., the count noun *rabbit* is interpreted as *rabbit meat*, while in 2. the plural form of the mass noun *wine* means *glasses of wine*. Both cases are examples of **coercion**, a semantic phenomenon occurring when the standard interpretation of an expression (in our case, the noun) yields an impossible conceptual representation (e.g. in 1. a rabbit swimming in the soup) (Wiese and Maling, 2005); consequently, a more plausible interpretation is retrieved by "enriching" the semantic representation with concepts that are associated to the standard interpretation of the target expression (*enriched composition*; see Jackendoff (1997)). The focus of this paper is specifically on nominal coercion of mass and count nouns.

Since it is rare to find nouns that occur exclu-
sively in either mass or count contexts, it makes
more sense to talk about predominantly count and
predominantly mass nouns. Chierchia (2010) de-
scribes the idea of mass-count *elasticity*, meaning
that any noun can be in principle mass or count,
its status being determined at the level of the nom-
inal phrase. When we say "predominantly" mass
or count noun, therefore, we mean that a noun has
the tendency to occur more frequently in one of the
two context types. On such basis, the count-mass
distinction can be intuitively seen as a continuum,
with the nouns traditionally described in the liter-
ature being closer to one the two extremes (Katz
and Zamparelli, 2012).

In this work, we investigate to what extent mod-
ern *Distributional Semantic Models* -which are
nowadays the standard for the representation of
lexical meaning in NLP- encode the meaning shifts
caused by mass-count coercion. We run two dif-
ferent experiments, making use respectively of
static and contextualized word embedding mod-
els to identify the meaning shifts, and we study
some of the potential factors that might influence
the extent to which a noun is shifting.

## 2 Related Work

Modern NLP widely adopts Distributional Seman-
tic Models (DSMs) for the representation of lexical
meaning, using vectors that are based on the co-
occurrences patterns of words in large text corpora.
Vector representations are usually compared us-
ing the cosine of the angle between them, and the
smaller the angle between two words, the closer
their meanings will be (Turney and Pantel, 2010).

The literature on DSMs has identified three gen-
erations of vector spaces (Lenci et al., 2022). The
first generation is typically referred to as count
models (Baroni et al., 2014), because the spaces
are obtained from the extraction of co-occurrences
between the target words and the linguistic contexts
that are deemed relevant, then the co-occurrences
are weighted via associations measures (Landauer
and Dumais, 1997; Baroni and Lenci, 2010; Bulli-
naria and Levy, 2012).

A second family of models emerged in the early
2010s and became known as *word embeddings* or
*prediction-based* models (Mikolov et al., 2013; Bo-
janowski et al., 2017). In such models, the learning
of word vectors is generally framed as a super-
vised task: a neural network is trained to predict
words given other context words, and the vectors
are learned as parameters. Words that tend to co-
occur will have similar vector representations.

However, a common feature of both families is
that they produce *static* vector representations, in
the sense that each word gets represented as a sin-
gle vector, which makes it difficult to handle cases
of ambiguity and polysemy. The most recent gen-
eration of distributional vectors is said instead to
be *contextualized*, because word representations
are generated in context on the basis of the activa-
tion states of a neural language model (Peters et al.,
2018; Devlin et al., 2019). One of the advantages of
models like BERT (Devlin et al., 2019) is that they
allow generating a specific word embedding for
each context in which target words occur, making
them an interesting option for modeling contextual
phenomena such as nominal coercion.

Concerning the modeling work on nominal coer-
cion in Distributional Semantics, Katz and Zampar-
elli (2012) were the first, to our knowledge, to use

DSMs to investigate the phenomenon. They con-
sidered pluralisation as a proxy of count usage, and
built a traditional count model with separate vector
representations for the singular and the plural of
a list of candidate mass and count nouns. Consis-
tently with their initial hypothesis, they found that
the vector similarity between singular and plural is
higher for count nouns than for mass nouns, since
the latter undergo a meaning shift when they are
pluralized (cf. example 2 in Section 1). Hürlimann
et al. (2014) later analyzed the factors affecting the
similarity scores in the data by Katz and Zamparelli
(2012), reporting that abstract and highly polyse-
mous nouns undergo greater semantic shifts as a
consequence of pluralization.

Both these works are close in spirit to our re-
search: in our first experiment, we will use sev-
eral types of word embedding models to compare
the distances between singular and plural forms
of mass and count nouns; in our second experi-
ment, we will use the contextualized vectors of
BERT to observe how coercion changes the se-
mantic representations of the nouns in mass and
count contexts, which we automatically extract
from the British National Corpus (Leech, 1992).
To our knowledge, this is the first study specifi-
cally on mass-count nominal coercion including
both static and contextualized embedding mod-
els, although other types of coercion have previ-
ously been investigated in the literature on DSMs,
e.g. complement coercion (Zarcone and Padó,
2011; Chersoni et al., 2017; Rambelli et al., 2020;
Chersoni et al., 2021; Ye et al., 2022) or classi-
cal metonymies (CONTAINER-FOR-CONTENT,
PRODUCER FOR PRODUCT etc.) (Pedinotti and
Lenci, 2020).

3 Experiment 1: Comparing the
Singular-Plural Similarity in Static
Embedding Spaces

In our first experiment, we follow Katz and Zampar-
elli (2012) in considering pluralisation as a reliable
proxy of count usage and we compare the distribu-
tional representations of singular and plural forms
of candidate mass and count nouns across the most
popular word embedding spaces in the literature. If
a model is able to detect the coercion meaning shift,
then we expect to see that the average semantic sim-
ilarity between singular and plural forms is lower
for the mass nouns (see example 2 in Section 1).
We use a list of predominantly count and predom-
static and contextualized word embedding mod-
els to identify the meaning shifts, and we study
some of the potential factors that might influence
the extent to which a noun is shifting.

## 2 Related Work

Modern NLP widely adopts Distributional Seman-
tic Models (DSMs) for the representation of lexical
meaning, using vectors that are based on the co-
occurrences patterns of words in large text corpora.
Vector representations are usually compared us-
ing the cosine of the angle between them, and the
smaller the angle between two words, the closer
their meanings will be (Turney and Pantel, 2010).

The literature on DSMs has identified three gen-
erations of vector spaces (Lenci et al., 2022). The
first generation is typically referred to as count
models (Baroni et al., 2014), because the spaces
are obtained from the extraction of co-occurrences
between the target words and the linguistic contexts
that are deemed relevant, then the co-occurrences
are weighted via associations measures (Landauer
and Dumais, 1997; Baroni and Lenci, 2010; Bulli-
naria and Levy, 2012).

A second family of models emerged in the early
2010s and became known as *word embeddings* or
*prediction-based* models (Mikolov et al., 2013; Bo-
janowski et al., 2017). In such models, the learning
of word vectors is generally framed as a super-
vised task: a neural network is trained to predict
words given other context words, and the vectors
are learned as parameters. Words that tend to co-
occur will have similar vector representations.

However, a common feature of both families is
that they produce *static* vector representations, in
the sense that each word gets represented as a sin-
gle vector, which makes it difficult to handle cases
of ambiguity and polysemy. The most recent gen-
eration of distributional vectors is said instead to
be *contextualized*, because word representations
are generated in context on the basis of the activa-
tion states of a neural language model (Peters et al.,
2018; Devlin et al., 2019). One of the advantages of
models like BERT (Devlin et al., 2019) is that they
allow generating a specific word embedding for
each context in which target words occur, making
them an interesting option for modeling contextual
phenomena such as nominal coercion.

Concerning the modeling work on nominal coer-
cion in Distributional Semantics, Katz and Zampar-
elli (2012) were the first, to our knowledge, to use

DSMs to investigate the phenomenon. They con-
sidered pluralisation as a proxy of count usage, and
built a traditional count model with separate vector
representations for the singular and the plural of
a list of candidate mass and count nouns. Consis-
tently with their initial hypothesis, they found that
the vector similarity between singular and plural is
higher for count nouns than for mass nouns, since
the latter undergo a meaning shift when they are
pluralized (cf. example 2 in Section 1). Hürlimann
et al. (2014) later analyzed the factors affecting the
similarity scores in the data by Katz and Zamparelli
(2012), reporting that abstract and highly polyse-
mous nouns undergo greater semantic shifts as a
consequence of pluralization.

Both these works are close in spirit to our re-
search: in our first experiment, we will use sev-
eral types of word embedding models to compare
the distances between singular and plural forms
of mass and count nouns; in our second experi-
ment, we will use the contextualized vectors of
BERT to observe how coercion changes the se-
mantic representations of the nouns in mass and
count contexts, which we automatically extract
from the British National Corpus (Leech, 1992).
To our knowledge, this is the first study specifi-
cally on mass-count nominal coercion including
both static and contextualized embedding mod-
els, although other types of coercion have previ-
ously been investigated in the literature on DSMs,
e.g. complement coercion (Zarcone and Padó,
2011; Chersoni et al., 2017; Rambelli et al., 2020;
Chersoni et al., 2021; Ye et al., 2022) or classi-
cal metonymies (CONTAINER-FOR-CONTENT,
PRODUCER FOR PRODUCT etc.) (Pedinotti and
Lenci, 2020).

3 Experiment 1: Comparing the
Singular-Plural Similarity in Static
Embedding Spaces

In our first experiment, we follow Katz and Zampar-
elli (2012) in considering pluralisation as a reliable
proxy of count usage and we compare the distribu-
tional representations of singular and plural forms
of candidate mass and count nouns across the most
popular word embedding spaces in the literature. If
a model is able to detect the coercion meaning shift,
then we expect to see that the average semantic sim-
ilarity between singular and plural forms is *lower*
for the mass nouns (see example 2 in Section 1).
We use a list of predominantly count and predom-
<figure><img src="image_18.png" /><figcaption>Figure 1: Cosine similarity scores for the singular-plural comparison of count and mass nouns in the GloVe-w2 (left) and in the GloVe-w10 model (right).</figcaption></figure>

inantly mass nouns introduced in the same paper, identified via the selection of syntactic contexts:

* 1a. candidate mass nouns: *information, time, money, detail, space, fun, attention, info, part, work, interest, evidence, experience, energy, power, water, room, recipe, use, opportunity, effort, emphasis, support, research, trouble*;

* 1b. candidate count nouns: *time, year, day, way, person, place, bit, week, man, opportunity, problem, lot, thing, role, company, basis, child, look, one, report, month, book, area, approach, hour*.

The vectors of the target nouns and their corresponding plural forms are firstly extracted from different word embedding spaces. Our pool of models includes the following vector spaces: CBOW vectors (Mikolov et al., 2013), one model with window size 2 (CBOW-w2) and one with window size 10 (CBOW-w10); Skip-Gram vectors (Mikolov et al., 2013), one model with window size 2 (SGNS-w2) and one with window size 10 (SGNS-w10); GloVe vectors (Pennington et al., 2014), one model with window size 2 (GloVe-w2) and one with window size 10 (GloVe-w10); FastText vectors (Bojanowski et al., 2017), one model with window size 2 (FastText-w2) and one with window size 10 (FastText-w10). Finally, we also include two variants of the Skip Gram where the contexts are selected via syntactic dependency with the target word (Levy and Goldberg, 2014; Lenci et al., 2022), one with untyped dependencies (SGNS-synf) and one with typed dependencies (SGNS-synt) (e.g. in

the first case, given the target *dog* and the context *big dog*, the model will just use the syntactic neighbor *big* as a context, while the second will also include the type of syntactic relation linking the two words, i.e. adjectival modifier). All models have been trained with default hyperparameters on a concatenation of the UkWac (Baroni et al., 2009), of the British National Corpus and of a 2018 dump of Wikipedia <fnref n="1" />, and the semantic similarity is estimated via the classical cosine metric.

<table><caption>Table 1: Average of cosine similarity scores between singular and plural forms for each vector space, and *p*-values computed on the scores of mass and count nouns. Significant differences are reported as follows: *p* < 0.05*, *p* < 0.01 **, *p* < 0.001 ***.</caption><thead><tr><th>Model</th><th>Avg. mass</th><th>Avg. count</th><th>p</th></tr></thead><tbody><tr><td>CBOW-w2</td><td>0.59</td><td>0.60</td><td></td></tr><tr><td>CBOW-w10</td><td>0.54</td><td>0.56</td><td></td></tr><tr><td>FastText-w2</td><td>0.66</td><td>0.68</td><td></td></tr><tr><td>FastText-w10</td><td>0.69</td><td>0.74</td><td>*</td></tr><tr><td>GloVe-w2</td><td>0.56</td><td>0.71</td><td>***</td></tr><tr><td>GloVe-w10</td><td>0.60</td><td>0.75</td><td>***</td></tr><tr><td>SGNS-w2</td><td>0.64</td><td>0.66</td><td></td></tr><tr><td>SGNS-w10</td><td>0.62</td><td>0.67</td><td>*</td></tr><tr><td>SGNS-synf</td><td>0.66</td><td>0.66</td><td></td></tr><tr><td>SGNS-synt</td><td>0.67</td><td>0.65</td><td></td></tr></tbody></table>

We report the average similarity scores between singular and plural forms for both mass and count nouns in Table 1, and we use the Wilcoxon rank sum test to identify significant differences between the two groups. While for most models the scores are very close, 4 of them manage to identify a sig-

<footnote n="1">¹The corpus was POS-tagged and parsed and contains syntactic annotations in the Universal Dependencies format (Nivre et al., 2016; De Marneffe et al., 2021).</footnote>
<figure><img src="image_18.png" /><figcaption>Figure 1: Cosine similarity scores for the singular-plural comparison of count and mass nouns in the GloVe-w2 (left) and in the GloVe-w10 model (right).</figcaption></figure>

inantly mass nouns introduced in the same paper, identified via the selection of syntactic contexts:

* 1a. candidate mass nouns: *information, time, money, detail, space, fun, attention, info, part, work, interest, evidence, experience, energy, power, water, room, recipe, use, opportunity, effort, emphasis, support, research, trouble*;

* 1b. candidate count nouns: *time, year, day, way, person, place, bit, week, man, opportunity, problem, lot, thing, role, company, basis, child, look, one, report, month, book, area, approach, hour*.

The vectors of the target nouns and their corresponding plural forms are firstly extracted from different word embedding spaces. Our pool of models includes the following vector spaces: CBOW vectors (Mikolov et al., 2013), one model with window size 2 (CBOW-w2) and one with window size 10 (CBOW-w10); Skip-Gram vectors (Mikolov et al., 2013), one model with window size 2 (SGNS-w2) and one with window size 10 (SGNS-w10); GloVe vectors (Pennington et al., 2014), one model with window size 2 (GloVe-w2) and one with window size 10 (GloVe-w10); FastText vectors (Bojanowski et al., 2017), one model with window size 2 (FastText-w2) and one with window size 10 (FastText-w10). Finally, we also include two variants of the Skip Gram where the contexts are selected via syntactic dependency with the target word (Levy and Goldberg, 2014; Lenci et al., 2022), one with untyped dependencies (SGNS-synf) and one with typed dependencies (SGNS-synt) (e.g. in

the first case, given the target *dog* and the context *big dog*, the model will just use the syntactic neighbor *big* as a context, while the second will also include the type of syntactic relation linking the two words, i.e. adjectival modifier). All models have been trained with default hyperparameters on a concatenation of the UkWac (Baroni et al., 2009), of the British National Corpus and of a 2018 dump of Wikipedia <fnref n="1" />, and the semantic similarity is estimated via the classical cosine metric.

<table><caption>Table 1: Average of cosine similarity scores between singular and plural forms for each vector space, and *p*-values computed on the scores of mass and count nouns. Significant differences are reported as follows: *p* < 0.05*, *p* < 0.01 **, *p* < 0.001 ***.</caption><thead><tr><th>Model</th><th>Avg. mass</th><th>Avg. count</th><th>p</th></tr></thead><tbody><tr><td>CBOW-w2</td><td>0.59</td><td>0.60</td><td></td></tr><tr><td>CBOW-w10</td><td>0.54</td><td>0.56</td><td></td></tr><tr><td>FastText-w2</td><td>0.66</td><td>0.68</td><td></td></tr><tr><td>FastText-w10</td><td>0.69</td><td>0.74</td><td>*</td></tr><tr><td>GloVe-w2</td><td>0.56</td><td>0.71</td><td>***</td></tr><tr><td>GloVe-w10</td><td>0.60</td><td>0.75</td><td>***</td></tr><tr><td>SGNS-w2</td><td>0.64</td><td>0.66</td><td></td></tr><tr><td>SGNS-w10</td><td>0.62</td><td>0.67</td><td>*</td></tr><tr><td>SGNS-synf</td><td>0.66</td><td>0.66</td><td></td></tr><tr><td>SGNS-synt</td><td>0.67</td><td>0.65</td><td></td></tr></tbody></table>

We report the average similarity scores between singular and plural forms for both mass and count nouns in Table 1, and we use the Wilcoxon rank sum test to identify significant differences between the two groups. While for most models the scores are very close, 4 of them manage to identify a sig-

<footnote n="1">¹The corpus was POS-tagged and parsed and contains syntactic annotations in the Universal Dependencies format (Nivre et al., 2016; De Marneffe et al., 2021).</footnote>
nificant difference and in all cases the similarity is lower for the mass nouns. GloVe models are the ones finding the biggest differences, with the scores of mass nouns being significantly lower (see the boxplots in Figure 1). Interestingly, among the embedding models, GloVe is the only one belonging to the more traditional count-based types, and thus more similar to the ones used in the studies of Katz and Zamparelli (2012) and Hürlimann et al. (2014). This may suggest that the GloVe training method, based on global co-occurrence statistics, is a better fit for capturing fine-grained semantic differences than the vectors derived from the Word2Vec family, which are all trained on separate local context windows. Additionally, larger differences are found by the models with a larger window, suggesting that semantic shifts are better captured by vector spaces modeling topic/domain similarity (Turney, 2012). On the other hand, vector spaces modeling local contextual co-occurrences fail to find any difference between mass and count nouns.

## 4 Experiment 2: Modeling Mass/Count Coercion with BERT

In our second experiment, we extract sentences in which our mass and count nouns occur from the British National Corpus, and we use the patterns described in Katz and Zamparelli (2012) to divide them into mass contexts and count contexts, and then we use the BERT model to compare their con-textualized representations. Using BERT allows us to take into account a wider variety of contexts rather than just using a pair of vectors for the sin-gular and plural forms. In this case, we expect that both types of nouns, when they occur in different context types, will have a lower semantic similarity, because both of them will be undergoing semantic shifts (mass to count or count to mass).

The selected patterns are the following:

* 2a. mass: i) singular nouns immediately be preceded by *lots*, *plenty of*, *much*, *more*, *less*, *enough*, *most*, *sufficient*, *considerable*, *bound-less*, *ample*, or *limited* that are not preceded by *a(n)*; ii) singular nouns directly following a verb;

* 2b. count: i) singular nouns immediately be preceded by *a*, *an*, *one*, *every*, *first*, *each*, *an-other*; ii) plural nouns.

In both contexts, the nouns are excluded if followed by another noun, adjective, or participle to

avoid selecting noun-noun compounds. In mass contexts, we also exclude the cases where the target nouns directly follow a participle to prevent misclassification of the participle noun phrases, e.g. *the baked cake*. To increase the reliability of the sentences for the experiment, we manually filter the sentences containing cases of idiomatic usages, e.g. *day by day*. As a result, we extract a total of 614512 sentences. Only the candidate mass or count nouns occurring at least 20 times in both mass and count contexts are considered. Generally, count nouns have a higher average frequency in both contexts, and both count and mass nouns have a higher average frequency in count contexts. The frequency of count nouns in count context ranges from 49218 (*time*) to 6148 (*role*), with a mean of 27787.47, while the frequency in mass context ranges from 8211(*time*) to 20 (*role*), with a mean of 1245.53. For mass nouns, the width of frequency in mass context is from 9259 (*part*) to 45 (*recipe*) and the average frequency is 2110.43, whereas the mass nouns in count contexts have an average frequency of 6414.19, a maximum of 49598 (*time*) and a minimum of 34 (*information*). Notice that nouns can, in principle, occur both as count and as mass nouns, and their frequencies are computed separately as they have been extracted with different patterns. Among the target nouns, *time* and *opportunity* appear as both candidate count noun and candidate mass noun. Although the nouns may be argued as ambiguous, the syntactic patterns used to extract them are unambiguous and can correctly reflect their usage in the count contexts and mass contexts.

² Therefore, they could still be included to compare the meaning shift a noun undergoes in the transition from the 'standard' context to coerced context.

<table><caption>Table 2: Statistics for the context extraction from the British National Corpus: average, max and min frequency for each noun-context type.</caption><thead><tr><th>Noun</th><th>Context</th><th>Avg. freq</th><th>Avg. freq</th><th>Min. freq</th></tr></thead><tbody><tr><td>Count</td><td>Count</td><td>27787.47</td><td>49218</td><td>6148</td></tr><tr><td>Count</td><td>Mass</td><td>1245.53</td><td>8211</td><td>20</td></tr><tr><td>Mass</td><td>Mass</td><td>2110.43</td><td>9259</td><td>45</td></tr><tr><td>Mass</td><td>Count</td><td>6414.49</td><td>48598</td><td>34</td></tr></tbody></table>

Then we use the BERT-BASE-UNCASED model and the MINICONS Python library (Misra, 2022) <fnref n="3" /> to generate semantic representations of the target

<footnote n="2">²We thank the first anonymous reviewer for pointing out this issue.</footnote>

<footnote n="3">³https://github.com/kanishkamisra/minicons</footnote>
nificant difference and in all cases the similarity is lower for the mass nouns. GloVe models are the ones finding the biggest differences, with the scores of mass nouns being significantly lower (see the boxplots in Figure 1). Interestingly, among the embedding models, GloVe is the only one belonging to the more traditional count-based types, and thus more similar to the ones used in the studies of Katz and Zamparelli (2012) and Hürlimann et al. (2014). This may suggest that the GloVe training method, based on global co-occurrence statistics, is a better fit for capturing fine-grained semantic differences than the vectors derived from the Word2Vec family, which are all trained on separate local context windows. Additionally, larger differences are found by the models with a larger window, suggesting that semantic shifts are better captured by vector spaces modeling topic/domain similarity (Turney, 2012). On the other hand, vector spaces modeling local contextual co-occurrences fail to find any difference between mass and count nouns.

## 4 Experiment 2: Modeling Mass/Count Coercion with BERT

In our second experiment, we extract sentences in which our mass and count nouns occur from the British National Corpus, and we use the patterns described in Katz and Zamparelli (2012) to divide them into mass contexts and count contexts, and then we use the BERT model to compare their contextualized representations. Using BERT allows us to take into account a wider variety of contexts rather than just using a pair of vectors for the singular and plural forms. In this case, we expect that both types of nouns, when they occur in different context types, will have a lower semantic similarity, because both of them will be undergoing semantic shifts (mass to count or count to mass).

The selected patterns are the following:

* 2a. mass: i) singular nouns immediately be preceded by *lots*, *plenty of*, *much*, *more*, *less*, *enough*, *most*, *sufficient*, *considerable*, *boundless*, *ample*, or *limited* that are not preceded by *a(n)*; ii) singular nouns directly following a verb;

* 2b. count: i) singular nouns immediately be preceded by *a*, *an*, *one*, *every*, *first*, *each*, *another*; ii) plural nouns.

In both contexts, the nouns are excluded if followed by another noun, adjective, or participle to

avoid selecting noun-noun compounds. In mass contexts, we also exclude the cases where the target nouns directly follow a participle to prevent misclassification of the participle noun phrases, e.g. *the baked cake*. To increase the reliability of the sentences for the experiment, we manually filter the sentences containing cases of idiomatic usages, e.g. *day by day*. As a result, we extract a total of 614512 sentences. Only the candidate mass or count nouns occurring at least 20 times in both mass and count contexts are considered. Generally, count nouns have a higher average frequency in both contexts, and both count and mass nouns have a higher average frequency in count contexts. The frequency of count nouns in count context ranges from 49218 (*time*) to 6148 (*role*), with a mean of 27787.47, while the frequency in mass context ranges from 8211(*time*) to 20 (*role*), with a mean of 1245.53. For mass nouns, the width of frequency in mass context is from 9259 (*part*) to 45 (*recipe*) and the average frequency is 2110.43, whereas the mass nouns in count contexts have an average frequency of 6414.19, a maximum of 49598 (*time*) and a minimum of 34 (*information*). Notice that nouns can, in principle, occur both as count and as mass nouns, and their frequencies are computed separately as they have been extracted with different patterns. Among the target nouns, *time* and *opportunity* appear as both candidate count noun and candidate mass noun. Although the nouns may be argued as ambiguous, the syntactic patterns used to extract them are unambiguous and can correctly reflect their usage in the count contexts and mass contexts.

² Therefore, they could still be included to compare the meaning shift a noun undergoes in the transition from the 'standard' context to coerced context.

<table><caption>Table 2: Statistics for the context extraction from the British National Corpus: average, max and min frequency for each noun-context type.</caption><thead><tr><th>Noun</th><th>Context</th><th>Avg. freq</th><th>Avg. freq</th><th>Min. freq</th></tr></thead><tbody><tr><td>Count</td><td>Count</td><td>27787.47</td><td>49218</td><td>6148</td></tr><tr><td>Count</td><td>Mass</td><td>1245.53</td><td>8211</td><td>20</td></tr><tr><td>Mass</td><td>Mass</td><td>2110.43</td><td>9259</td><td>45</td></tr><tr><td>Mass</td><td>Count</td><td>6414.49</td><td>48598</td><td>34</td></tr></tbody></table>

Then we use the BERT-BASE-UNCASED model and the MINICONS Python library (Misra, 2022) <fnref n="3" /> to generate semantic representations of the target

<footnote n="2">²We thank the first anonymous reviewer for pointing out this issue.</footnote>

<footnote n="3">³https://github.com/kanishkamisra/minicons</footnote>
nouns in context: the idea is to measure the similarity scores of each (mass or count) noun to itself for randomly sampled sentences. We carry out the sampling either i) by selecting context pairs where the target noun occurs in both cases in its mass, or in its count contexts (within the same context type, which could be either count or mass); or ii) by selecting context pairs where the target noun occurs once in a mass context and once in a count context (*between* context types).

This means that each noun type will have its occurrences sampled in three different ways:

1. all context pairs sampled from its own type (mass nouns in mass contexts, count nouns in count contexts);

2. all context pairs sampled from the other type (mass nouns in count contexts, count nouns in mass contexts);

3. the context pair composed by one mass context and one count context.

The similarity comparison between 1) and 3) is the most relevant one for our study: we expect that similarities in 3) to be much lower than in 1), to an extent proportional to the meaning shift that the noun is undergoing. For each noun, we repeat the sampling 10 times from each group, and for each time we randomly extract 10 different context pairs to generate the vectors.

Notice that, differently from a big part of the literature, we use Spearman's rank correlation and not the cosine as a similarity metric for BERT vectors. Our choice is motivated by recent findings about the anisotropy of contextualized vector spaces, where a small number of 'rogue' dimensions dominate the cosine similarity scores (Ethyarajh, 2019; Timkey and van Schijndel, 2021). Timkey and van Schijndel (2021) showed that using postprocessing techniques like normalization or rank-based metrics such as Spearman's rank led to much better correlations with human similarity judgments. Moreover, rank-based metrics have been previously proven to be more robust than cosine in several similarity-related tasks (Santus et al., 2016a,b, 2017, 2018; Zhelezniak et al., 2019).

The results of Spearman's rank correlation experiment are reported in Table 3. The average correlation of context pairs where the target noun occurs in its typical kind of context (i.e. count nouns in count contexts, mass nouns in the mass ones) reflects

<table><caption>Table 3: Average Spearman's rank correlation scores for each noun type under the six different sampling conditions.</caption><thead><tr><th>Noun</th><th>Context</th><th>Avg. corr</th></tr></thead><tbody><tr><td>Count</td><td>Count</td><td>0.455</td></tr><tr><td>Count</td><td>Mass</td><td>0.466</td></tr><tr><td>Count</td><td>Both</td><td>0.360</td></tr><tr><td>Mass</td><td>Mass</td><td>0.550</td></tr><tr><td>Mass</td><td>Count</td><td>0.476</td></tr><tr><td>Mass</td><td>Both</td><td>0.391</td></tr></tbody></table>

how semantically similar the target noun is to itself when used in the 'standard' meaning, while the average correlation across different context types reflects the similarity between the standard and the coerced meaning. Therefore, the difference between the two correlations should quantify the meaning shift of the target noun when nominal coercion is imposed on the standard interpretation.

Let us illustrate the statement with an example for the predominantly count noun *problem* and an example for the predominantly mass noun *water*, respectively.

s1. more, i believe, than would be acceptable to people, so that nuclear power in itself will never be the solution to our energy *problems*. (count context)

s2. not surprisingly these devices are distributed with little or no instruction on correct use — thus increasing women's health *problems*. (count context)

s3. current models seem to be auto-sensing, so there shouldn't be much *problem*. (mass context)

The noun *problems* in s1 and s2 refer in both cases to a specific issue that needs to be resolved, while *problem* in s3 seems to be more generic and more similar to *trouble*. Accordingly, the correlation of s1 and s2 is 0.55, and the correlation of s1 and s3 is 0.33. The correlation difference between the s1-s2 pair and the s1-s3 pair should reflect the meaning shift that *problem* undergoes, changing from its standard "count" meaning to its coerced interpretation in a mass context.

s4. you can drink *water* freely during the course of the diet. (mass context)

s5. in the year to august 1992, the works used 22 per cent less *water*, 18 per cent less nitrogen, 11 per cent less steam and nine per cent less electricity. (mass context)

s6. to the east of Venice lies Lido di Jesolo and Caorle, with miles of golden sand lapped by the warm *waters* of the Adriatic. (count context)

The noun *water* in s4 and s5 is used in mass contexts and it has the standard meaning of i) *water* as
nouns in context: the idea is to measure the similarity scores of each (mass or count) noun to itself for randomly sampled sentences. We carry out the sampling either i) by selecting context pairs where the target noun occurs in both cases in its mass, or in its count contexts (within the same context type, which could be either count or mass); or ii) by selecting context pairs where the target noun occurs once in a mass context and once in a count context (*between* context types).

This means that each noun type will have its occurrences sampled in three different ways:

1. all context pairs sampled from its own type (mass nouns in mass contexts, count nouns in count contexts);

2. all context pairs sampled from the other type (mass nouns in count contexts, count nouns in mass contexts);

3. the context pair composed by one mass context and one count context.

The similarity comparison between 1) and 3) is the most relevant one for our study: we expect that similarities in 3) to be much lower than in 1), to an extent proportional to the meaning shift that the noun is undergoing. For each noun, we repeat the sampling 10 times from each group, and for each time we randomly extract 10 different context pairs to generate the vectors.

Notice that, differently from a big part of the literature, we use Spearman's rank correlation and not the cosine as a similarity metric for BERT vectors. Our choice is motivated by recent findings about the anisotropy of contextualized vector spaces, where a small number of 'rogue' dimensions dominate the cosine similarity scores (Ethyarajh, 2019; Timkey and van Schijndel, 2021). Timkey and van Schijndel (2021) showed that using postprocessing techniques like normalization or rank-based metrics such as Spearman's rank led to much better correlations with human similarity judgments. Moreover, rank-based metrics have been previously proven to be more robust than cosine in several similarity-related tasks (Santus et al., 2016a,b, 2017, 2018; Zhelezniak et al., 2019).

The results of Spearman's rank correlation experiment are reported in Table 3. The average correlation of context pairs where the target noun occurs in its typical kind of context (i.e. count nouns in count contexts, mass nouns in the mass ones) reflects

<table><caption>Table 3: Average Spearman's rank correlation scores for each noun type under the six different sampling conditions.</caption><thead><tr><th>Noun</th><th>Context</th><th>Avg. corr</th></tr></thead><tbody><tr><td>Count</td><td>Count</td><td>0.455</td></tr><tr><td>Count</td><td>Mass</td><td>0.466</td></tr><tr><td>Count</td><td>Both</td><td>0.360</td></tr><tr><td>Mass</td><td>Mass</td><td>0.550</td></tr><tr><td>Mass</td><td>Count</td><td>0.476</td></tr><tr><td>Mass</td><td>Both</td><td>0.391</td></tr></tbody></table>

how semantically similar the target noun is to itself when used in the 'standard' meaning, while the average correlation across different context types reflects the similarity between the standard and the coerced meaning. Therefore, the difference between the two correlations should quantify the meaning shift of the target noun when nominal coercion is imposed on the standard interpretation.

Let us illustrate the statement with an example for the predominantly count noun *problem* and an example for the predominantly mass noun *water*, respectively.

s1. more, i believe, than would be acceptable to people, so that nuclear power in itself will never be the solution to our energy *problems*. (count context)

s2. not surprisingly these devices are distributed with little or no instruction on correct use — thus increasing women's health *problems*. (count context)

s3. current models seem to be auto-sensing, so there shouldn't be much *problem*. (mass context)

The noun *problems* in s1 and s2 refer in both cases to a specific issue that needs to be resolved, while *problem* in s3 seems to be more generic and more similar to *trouble*. Accordingly, the correlation of s1 and s2 is 0.55, and the correlation of s1 and s3 is 0.33. The correlation difference between the s1-s2 pair and the s1-s3 pair should reflect the meaning shift that *problem* undergoes, changing from its standard "count" meaning to its coerced interpretation in a mass context.

s4. you can drink *water* freely during the course of the diet. (mass context)

s5. in the year to august 1992, the works used 22 per cent less *water*, 18 per cent less nitrogen, 11 per cent less steam and nine per cent less electricity. (mass context)

s6. to the east of Venice lies Lido di Jesolo and Caorle, with miles of golden sand lapped by the warm *waters* of the Adriatic. (count context)

The noun *water* in s4 and s5 is used in mass contexts and it has the standard meaning of i) *water* as
<figure><img src="image_19.png" /><figcaption>Figure 2: Spearman’s rank correlations for mass (left) and count (right) nouns computed with BERT-BASE-UNCASED. The dark green box shows the correlations for the same group (mass in mass, count in count) context sampling, the light green box shows the correlations with one mass and one count context.</figcaption></figure>

a liquid that can be drunk (s4); and ii) the amount of *water* usage in a hydraulic system (s5). On the other hand, in the last context (s6) *waters* is rather referring to a specific geographical/territorial unit. The correlation of the s4-s5 pair is 0.59, while the correlation of the s4-s6 pair is 0.41, and the correlation difference between the two pairs should reflect the meaning shift from mass to count usage.

It is immediately evident from Table 2 that 3 count nouns generally have a lower average Spearman correlation score than mass nouns in either count or mass contexts, suggesting that the cluster of count nouns is less compact and their meanings are more varied and scattered across the semantic space. It should be noticed that many of the count nouns are highly frequent (e.g. *child*, *thing*, *way*, *man*, *one*, *place*, *time*, *day* all have more than 10K occurrences, more than any mass noun in our data), therefore they might display much more contextual variation in their usage, which could explain the relatively low similarity value. Indeed, the similarity of the count nouns when the contexts are sampled from the count or the mass groups does not differ significantly, with the latter being even slightly higher.

For both count and mass nouns, as we expected, we observe a lower similarity when the contexts are sampled from both groups (see also Figure 2): the average difference with the sampling within the same group is, respectively, of 0.1 and 0.16 correlation points, confirming the finding that mass nouns typically undergo a greater meaning shift (Hürlimann et al., 2014) even when patterns beyond pluralization are taken into account.

It is also noticeable that mass nouns have a relatively high similarity when they are sampled in count contexts, which could be explained by the fact that many of these nouns have systematic secondary meanings that are more compatible with a count usage (nouns denoting drinkable liquids are typically undergoing a shift from the liquid to the container, e.g. *beers* → *pints of beer*, or from the liquid to the variety, e.g. *wines* → *varieties of wine*). In sum, the results of our experiment provide further support to the view that the mass/count distinction should be seen as a continuum, and that the syntactic context is the strongest cue to the type of denotation (Chierchia, 2010). Moreover, even with more varied mass and count contexts than the ones used in previous studies (Katz and Zamparelli, 2012; Hürlimann et al., 2014), we also find that coercion makes mass nouns undergo a greater semantic shift than count nouns.

We also analyze some of the factors mentioned as relevant by Hürlimann et al. (2014) to predict the meaning shift of the nouns: frequency, polysemy, and concreteness. For polysemy, we simply use the WordNet synsets (Fellbaum, 2010) of a noun as an indicator of the number of word senses, while for concreteness we use the values from the English norms by Brysbaert et al. (2014).

Unfortunately, we do not find any significant correlation between the average differences in the Spearman correlations that we computed with BERT and the above-mentioned factors, probably because of the small size of our set of nouns. Table 4 presents the statistics for the top-5 most shifting count and mass nouns (i.e. the nouns with the
<figure><img src="image_19.png" /><figcaption>Figure 2: Spearman’s rank correlations for mass (left) and count (right) nouns computed with BERT-BASE-UNCASED. The dark green box shows the correlations for the same group (mass in mass, count in count) context sampling, the light green box shows the correlations with one mass and one count context.</figcaption></figure>

a liquid that can be drunk (s4); and ii) the amount of *water* usage in a hydraulic system (s5). On the other hand, in the last context (s6) *waters* is rather referring to a specific geographical/territorial unit. The correlation of the s4-s5 pair is 0.59, while the correlation of the s4-s6 pair is 0.41, and the correlation difference between the two pairs should reflect the meaning shift from mass to count usage.

It is immediately evident from Table 2 that 3 count nouns generally have a lower average Spearman correlation score than mass nouns in either count or mass contexts, suggesting that the cluster of count nouns is less compact and their meanings are more varied and scattered across the semantic space. It should be noticed that many of the count nouns are highly frequent (e.g. *child*, *thing*, *way*, *man*, *one*, *place*, *time*, *day* all have more than 10K occurrences, more than any mass noun in our data), therefore they might display much more contextual variation in their usage, which could explain the relatively low similarity value. Indeed, the similarity of the count nouns when the contexts are sampled from the count or the mass groups does not differ significantly, with the latter being even slightly higher.

For both count and mass nouns, as we expected, we observe a lower similarity when the contexts are sampled from both groups (see also Figure 2): the average difference with the sampling within the same group is, respectively, of 0.1 and 0.16 correlation points, confirming the finding that mass nouns typically undergo a greater meaning shift (Hürlimann et al., 2014) even when patterns beyond pluralization are taken into account.

It is also noticeable that mass nouns have a relatively high similarity when they are sampled in count contexts, which could be explained by the fact that many of these nouns have systematic secondary meanings that are more compatible with a count usage (nouns denoting drinkable liquids are typically undergoing a shift from the liquid to the container, e.g. *beers* → *pints of beer*, or from the liquid to the variety, e.g. *wines* → *varieties of wine*). In sum, the results of our experiment provide further support to the view that the mass/count distinction should be seen as a continuum, and that the syntactic context is the strongest cue to the type of denotation (Chierchia, 2010). Moreover, even with more varied mass and count contexts than the ones used in previous studies (Katz and Zamparelli, 2012; Hürlimann et al., 2014), we also find that coercion makes mass nouns undergo a greater semantic shift than count nouns.

We also analyze some of the factors mentioned as relevant by Hürlimann et al. (2014) to predict the meaning shift of the nouns: frequency, polysemy, and concreteness. For polysemy, we simply use the WordNet synsets (Fellbaum, 2010) of a noun as an indicator of the number of word senses, while for concreteness we use the values from the English norms by Brysbaert et al. (2014).

Unfortunately, we do not find any significant correlation between the average differences in the Spearman correlations that we computed with BERT and the above-mentioned factors, probably because of the small size of our set of nouns. Table 4 presents the statistics for the top-5 most shifting count and mass nouns (i.e. the nouns with the
<table><caption>Table 4: Frequency, synsets and concreteness for the top-5 most shifting count (bold) and mass (italic) nouns.</caption><thead><tr><th>Noun</th><th>Freq.</th><th>WordNet Synsets</th><th>Concreteness</th></tr></thead><tbody><tr><td><strong>company</strong></td><td>18792</td><td>9</td><td>4.11</td></tr><tr><td><strong>child</strong></td><td>43018</td><td>4</td><td>4.78</td></tr><tr><td><strong>thing</strong></td><td>47655</td><td>12</td><td>3.17</td></tr><tr><td><strong>way</strong></td><td>29644</td><td>12</td><td>2.34</td></tr><tr><td><strong>area</strong></td><td>26273</td><td>6</td><td>3.72</td></tr><tr><td><em>information</em></td><td>3199</td><td>5</td><td>2.87</td></tr><tr><td><em>attention</em></td><td>2323</td><td>6</td><td>2.30</td></tr><tr><td><em>trouble</em></td><td>1051</td><td>6</td><td>2.25</td></tr><tr><td><em>support</em></td><td>308</td><td>11</td><td>2.83</td></tr><tr><td><em>money</em></td><td>4947</td><td>3</td><td>4.54</td></tr></tbody></table>

highest average correlation difference). Despite the lack of significance of the correlation scores, it can still be noticed that: i) regarding polysemy, the most shifting nouns tend to have a relatively high number of word senses; ii) as for concreteness, the most shifting count nouns have relatively high values, while the most shifting mass nouns tend to denote more abstract entities. More studies with a larger set of predominantly mass and count nouns are needed to confirm the finding.

## 4.1 A Final Note about Polysemy

With reference to our *rabbit meat* example (see Section 1) and as a general methodological consideration, Reviewer 2 points out that the, given the polysemy of the word *rabbit*, which is also attested in dictionaries, this example cannot be considered as a case of coercion, but it just corresponds to a different word sense. As a consequence, the polysemy of the target nouns should be identified in advance, because otherwise we risk to confuse coercion with occurrences of different word senses.

Since we are adopting the perspective of distributional approaches, in our view the main issue is whether linguistic distributions are *determined* by the inventory of senses of a word, or they are *determining* what we conceive as their inventory of senses, in accordance to the so-called strong versions of the Distributional Hypothesis (Miller and Charles, 1991; Lenci, 2008). In cases such the above-mentioned one, coercion itself might be responsible the emergence of new meanings and senses. Keeping the *rabbit meat* example, one could imagine that, following the same pattern, the speakers of a language at some point could start using the name for its meat in similar mass contexts,

and that would undoubtedly qualify as a case of coercion because the coerced meaning will be an innovation, and thus it would not be attested in any dictionary. Only when the usage of the name of the animal for its meat will have become frequent enough to be conventional, then dictionaries will start including it as a secondary sense.

This does not detract from the validity of the reviewer's objection. But we would like to clarify that, in our approach, we consider the word senses annotated in dictionaries and lexicographic resources as possibly consequential to shifts in linguistic distributions, and not the other way around.

## 5 Conclusion

In this paper, we have presented two experiments on modeling nominal coercion of mass and count nouns with two different typologies of Distributional Semantic Models. In the first experiment, we compared the vector representations of singular and plural mass/count nouns across several popular word embedding models. Perhaps surprisingly, we found that i) the count-based GloVe models and ii) the Word2Vec-like models with larger contextual windows were the most successful in identifying significant differences between singular and plural representations of mass nouns, whose meanings shifted more when they were pluralized, while the most of the other models did not detect any shift. We hypothesized, therefore, that such semantic shifts are better captured by semantic spaces that focus on modeling similarities of topic/domain, rather than similarity of co-occurrence in the same local contexts.

In the second experiment, we compared the vectors generated by BERT in different context
<table><caption>Table 4: Frequency, synsets and concreteness for the top-5 most shifting count (bold) and mass (italic) nouns.</caption><thead><tr><th>Noun</th><th>Freq.</th><th>WordNet Synsets</th><th>Concreteness</th></tr></thead><tbody><tr><td><strong>company</strong></td><td>18792</td><td>9</td><td>4.11</td></tr><tr><td><strong>child</strong></td><td>43018</td><td>4</td><td>4.78</td></tr><tr><td><strong>thing</strong></td><td>47655</td><td>12</td><td>3.17</td></tr><tr><td><strong>way</strong></td><td>29644</td><td>12</td><td>2.34</td></tr><tr><td><strong>area</strong></td><td>26273</td><td>6</td><td>3.72</td></tr><tr><td><em>information</em></td><td>3199</td><td>5</td><td>2.87</td></tr><tr><td><em>attention</em></td><td>2323</td><td>6</td><td>2.30</td></tr><tr><td><em>trouble</em></td><td>1051</td><td>6</td><td>2.25</td></tr><tr><td><em>support</em></td><td>308</td><td>11</td><td>2.83</td></tr><tr><td><em>money</em></td><td>4947</td><td>3</td><td>4.54</td></tr></tbody></table>

highest average correlation difference). Despite the lack of significance of the correlation scores, it can still be noticed that: i) regarding polysemy, the most shifting nouns tend to have a relatively high number of word senses; ii) as for concreteness, the most shifting count nouns have relatively high values, while the most shifting mass nouns tend to denote more abstract entities. More studies with a larger set of predominantly mass and count nouns are needed to confirm the finding.

## 4.1 A Final Note about Polysemy

With reference to our *rabbit meat* example (see Section 1) and as a general methodological consideration, Reviewer 2 points out that the, given the polysemy of the word *rabbit*, which is also attested in dictionaries, this example cannot be considered as a case of coercion, but it just corresponds to a different word sense. As a consequence, the polysemy of the target nouns should be identified in advance, because otherwise we risk to confuse coercion with occurrences of different word senses.

Since we are adopting the perspective of distributional approaches, in our view the main issue is whether linguistic distributions are *determined* by the inventory of senses of a word, or they are *determining* what we conceive as their inventory of senses, in accordance to the so-called strong versions of the Distributional Hypothesis (Miller and Charles, 1991; Lenci, 2008). In cases such the above-mentioned one, coercion itself might be responsible the emergence of new meanings and senses. Keeping the *rabbit meat* example, one could imagine that, following the same pattern, the speakers of a language at some point could start using the name for its meat in similar mass contexts,

and that would undoubtedly qualify as a case of coercion because the coerced meaning will be an innovation, and thus it would not be attested in any dictionary. Only when the usage of the name of the animal for its meat will have become frequent enough to be conventional, then dictionaries will start including it as a secondary sense.

This does not detract from the validity of the reviewer's objection. But we would like to clarify that, in our approach, we consider the word senses annotated in dictionaries and lexicographic resources as possibly consequential to shifts in linguistic distributions, and not the other way around.

## 5 Conclusion

In this paper, we have presented two experiments on modeling nominal coercion of mass and count nouns with two different typologies of Distributional Semantic Models. In the first experiment, we compared the vector representations of singular and plural mass/count nouns across several popular word embedding models. Perhaps surprisingly, we found that i) the count-based GloVe models and ii) the Word2Vec-like models with larger contextual windows were the most successful in identifying significant differences between singular and plural representations of mass nouns, whose meanings shifted more when they were pluralized, while the most of the other models did not detect any shift. We hypothesized, therefore, that such semantic shifts are better captured by semantic spaces that focus on modeling similarities of topic/domain, rather than similarity of co-occurrence in the same local contexts.

In the second experiment, we compared the vectors generated by BERT in different context
types. We found that the self-similarity of the nouns sharply decreased when contexts of different types were sampled to generate the contextualized representations and that the shifts of predominantly mass nouns were more pronounced. Our qualitative analyses suggested that factors such as polysemy and concreteness of the nouns might play a role in predicting semantic shifts, although more studies with a larger set of nouns are necessary.

Another promising direction for future research would be using DSMs to model the effects of nominal coercion in human sentence processing, since psycholinguistic studies proved that, in several languages (e.g. English, German, Mandarin Chinese), coerced nouns lead to increased reading times and longer eye fixations (McElree et al., 2001; Traxler et al., 2002; Pylkkanen and McElree, 2006; Zarcone et al., 2017; Xue et al., 2021). In this sense, integrating DSMs-derived similarity metrics in the current computational models could lead to better estimation of reading difficulties induced by coercion operations.

## Acknowledgements

This research was made possible by the start-up research fund (1-BD8S) at the Hong Kong Polytechnic University. We would also like to thank the two anonymous reviewers for their comments.

## References

Marco Baroni, Silvia Bernardini, Adriano Ferraresi, and Eros Zanchetta. 2009. The WaCky Wide Web: a Collection of Very Large Linguistically Processed Web-crawled Corpora. *Language Resources and Evaluation*, 43(3):209–226.

Marco Baroni, Georgiana Dinu, and Germán Kruszewski. 2014. Don't Count, Predict! a Systematic Comparison of Context-counting vs. Context-predicting Semantic Vectors. In *Proceedings of ACL*.

Marco Baroni and Alessandro Lenci. 2010. Distributional Memory: A General Framework for Corpus-based Semantics. *Computational Linguistics*, 36(4):673–721.

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching Word Vectors with Subword Information. *Transactions of the Association for Computational Linguistics*, 5:135–146.

Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman. 2014. Concreteness Ratings for 40 Thousand Generally Known English Word Lemmas. *Behavior Research Methods*, 46(3):904–911.

John A Bullinaria and Joseph P Levy. 2012. Extracting Semantic Representations from Word Co-occurrence Statistics: Stop-lists, Stemming, and SVD. *Behavior Research Methods*, 44(3):890–907.

Chung-Ying Cheng. 1973. Response to Moravcsik. *Approaches to Natural Language*.

Emmanuele Chersoni, Alessandro Lenci, and Philippe Blache. 2017. Logical Metonymy in a Distributional Model of Sentence Comprehension. In *Proceedings of *SEM*.

Emmanuele Chersoni, Enrico Santus, Alessandro Lenci, Philippe Blache, and Chu-Ren Huang. 2021. Not All Arguments Are Processed Equally: A Distributional Model of Argument Complexity. *Language Resources and Evaluation*, 55(4):873–900.

Gennaro Chierchia. 2010. Mass Nouns, Vagueness and Semantic Variation. *Synthese*, 174(1):99–149.

Noam Chomsky. 1965. *Aspects of the Theory of Syntax*. MIT Press.

Marie-Catherine De Marneffe, Christopher D Manning, Joakim Nivre, and Daniel Zeman. 2021. Universal Dependencies. *Computational Linguistics*, 47(2):255–308.

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In *Proceedings of NAACL*.

Kawin Ethayarajh. 2019. How Contextual Are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings. In *Proceedings of EMNLP*.

Christiane Fellbaum. 2010. Wordnet. In *Theory and Applications of Ontology: Computer Applications*, pages 231–243. Springer.

Manuela Hürlimann, Raffaella Bernardi, andonis Paperno. 2014. Nominal Coercion in Space: Mass/Count Nouns and Distributional Semantics. In *Proceedings of CLiC-it*.

Ray Jackendoff. 1997. *The Architecture of the Language Faculty*. 28. MIT Press.

Graham Katz and Roberto Zamparelli. 2012. Quantifying Count/Mass Elasticity. In *Proceedings of the West Coast Conference on Formal Linguistics*.

Thomas K Landauer and Susan T Dumais. 1997. A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. *Psychological Review*, 104(2):211.

Geoffrey Neil Leech. 1992. 100 Million Words of English: The British National Corpus (BNC). *Language Research*.
types. We found that the self-similarity of the nouns sharply decreased when contexts of different types were sampled to generate the contextualized representations and that the shifts of predominantly mass nouns were more pronounced. Our qualitative analyses suggested that factors such as polysemy and concreteness of the nouns might play a role in predicting semantic shifts, although more studies with a larger set of nouns are necessary.

Another promising direction for future research would be using DSMs to model the effects of nominal coercion in human sentence processing, since psycholinguistic studies proved that, in several languages (e.g. English, German, Mandarin Chinese), coerced nouns lead to increased reading times and longer eye fixations (McElree et al., 2001; Traxler et al., 2002; Pylkkanen and McElree, 2006; Zarcone et al., 2017; Xue et al., 2021). In this sense, integrating DSMs-derived similarity metrics in the current computational models could lead to better estimation of reading difficulties induced by coercion operations.

## Acknowledgements

This research was made possible by the start-up research fund (1-BD8S) at the Hong Kong Polytechnic University. We would also like to thank the two anonymous reviewers for their comments.

## References

Marco Baroni, Silvia Bernardini, Adriano Ferraresi, and Eros Zanchetta. 2009. The WaCky Wide Web: a Collection of Very Large Linguistically Processed Web-crawled Corpora. *Language Resources and Evaluation*, 43(3):209–226.

Marco Baroni, Georgiana Dinu, and Germán Kruszewski. 2014. Don't Count, Predict! a Systematic Comparison of Context-counting vs. Context-predicting Semantic Vectors. In *Proceedings of ACL*.

Marco Baroni and Alessandro Lenci. 2010. Distributional Memory: A General Framework for Corpus-based Semantics. *Computational Linguistics*, 36(4):673–721.

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching Word Vectors with Subword Information. *Transactions of the Association for Computational Linguistics*, 5:135–146.

Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman. 2014. Concreteness Ratings for 40 Thousand Generally Known English Word Lemmas. *Behavior Research Methods*, 46(3):904–911.

John A Bullinaria and Joseph P Levy. 2012. Extracting Semantic Representations from Word Co-occurrence Statistics: Stop-lists, Stemming, and SVD. *Behavior Research Methods*, 44(3):890–907.

Chung-Ying Cheng. 1973. Response to Moravcsik. *Approaches to Natural Language*.

Emmanuele Chersoni, Alessandro Lenci, and Philippe Blache. 2017. Logical Metonymy in a Distributional Model of Sentence Comprehension. In *Proceedings of *SEM*.

Emmanuele Chersoni, Enrico Santus, Alessandro Lenci, Philippe Blache, and Chu-Ren Huang. 2021. Not All Arguments Are Processed Equally: A Distributional Model of Argument Complexity. *Language Resources and Evaluation*, 55(4):873–900.

Gennaro Chierchia. 2010. Mass Nouns, Vagueness and Semantic Variation. *Synthese*, 174(1):99–149.

Noam Chomsky. 1965. *Aspects of the Theory of Syntax*. MIT Press.

Marie-Catherine De Marneffe, Christopher D Manning, Joakim Nivre, and Daniel Zeman. 2021. Universal Dependencies. *Computational Linguistics*, 47(2):255–308.

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In *Proceedings of NAACL*.

Kawin Ethayarajh. 2019. How Contextual Are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings. In *Proceedings of EMNLP*.

Christiane Fellbaum. 2010. Wordnet. In *Theory and Applications of Ontology: Computer Applications*, pages 231–243. Springer.

Manuela Hürlimann, Raffaella Bernardi, andonis Paperno. 2014. Nominal Coercion in Space: Mass/Count Nouns and Distributional Semantics. In *Proceedings of CLiC-it*.

Ray Jackendoff. 1997. *The Architecture of the Language Faculty*. 28. MIT Press.

Graham Katz and Roberto Zamparelli. 2012. Quantifying Count/Mass Elasticity. In *Proceedings of the West Coast Conference on Formal Linguistics*.

Thomas K Landauer and Susan T Dumais. 1997. A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. *Psychological Review*, 104(2):211.

Geoffrey Neil Leech. 1992. 100 Million Words of English: The British National Corpus (BNC). *Language Research*.
Alessandro Lenci. 2008. Distributional Semantics in Linguistic and Cognitive Research. *Italian Journal of Linguistics*, 20(1):1-31.

Alessandro Lenci, Magnus Sahlgren, Patrick Jeuniaux, Amaru Cuba Gyllensten, and Martina Miliani. 2022. A Comparative Evaluation and Analysis of Three Generations of Distributional Semantic Models. *Language Resources and Evaluation*, pages 1-45.

Omer Levy and Yoav Goldberg. 2014. Dependency-based Word Embeddings. In *Proceedings of ACL*.

Godehard Link. 1983. *The Logical Analysis of Plurals and Mass Terms: A Lattice-theoretical Approach*, volume 127. Blackwell Oxford.

Brian McElree, Matthew J Traxler, Martin J Pickering, Rachel E Seely, and Ray Jackendoff. 2001. Reading time evidence for enriched composition. *Cognition*, 78(1):B17-B25.

Tomas Mikolov, Kai Chen, Greg Corrado, and Jef-
frey Dean. 2013. Efficient Estimation of Word
Representations in Vector Space. *arXiv preprint*
*arXiv:1301.3781*.

George A Miller and Walter G Charles. 1991. Contextual Correlates of Semantic Similarity. *Language and Cognitive Processes*, 6(1):1-28.

Kanishka Misra. 2022. minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Models. *arXiv preprint arXiv:2203.13112*.

Joakim Nivre, Marie-Catherine De Marneffe, Filip Gin-
ter, Yoav Goldberg, Jan Hajic, Christopher D Man-
ning, Ryan McDonald, Slav Petrov, Sampo Pyysalo,
and Natalia Silveira. 2016. Universal Dependencies
v1: A Multilingual Treebank Collection. In *Proceed-
ings of LREC*.

Paolo Pedinotti and Alessandro Lenci. 2020. Don't Invite BERT to Drink a Bottle: Modeling the Interpretation of Metonymies Using BERT and Distributional Representations. In Proceedings of COLING.

Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global Vectors for Word Representation. In Proceedings of EMNLP.

Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep Contextualized Word Representations. In Proceedings of NAACL.

Liina Pylkkanen and Brian McElree. 2006. The Syntax-semantics Interface: On-line Composition of Sentence Meaning. *Handbook of Psycholinguistics*, pages 539–579.

Giulia Rambelli, Emmanuele Chersoni, Alessandro Lenci, Philippe Blache, and Chu-Ren Huang. 2020. Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation. In Proceedings of AACL-IJCNLP.

Enrico Santus, Emmanuele Chersoni, Alessandro Lenci, and Philippe Blache. 2017. Measuring Thematic Fit with Distributional Feature Overlap. In Proceedings of EMNLP.

Enrico Santus, Emmanuele Chersoni, Alessandro Lenci, Chu-Ren Huang, and Philippe Blache. 2016a. Testing APsyn against Vector Cosine on Similarity Estimation. In Proceedings of PACLIC.

Enrico Santus, Tin-Shing Chiu, Qin Lu, Alessandro Lenci, and Chu-Ren Huang. 2016b. What a Nerd! Beating Students and Vector Cosine in the ESL and TOEFL Datasets. In Proceedings of LREC.

Enrico Santus, Hongmin Wang, Emmanuele Chersoni, and Yue Zhang. 2018. A Rank-Based Similarity Metric for Word Embeddings. In Proceedings of ACL.

William Timkey and Marten van Schijndel. 2021. All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality. In Proceedings of EMNLP.

Matthew J Traxler, Martin J Pickering, and Brian McEl-
ree. 2002. Coercion in Sentence Processing: Evi-
dence from Eye-movements and Self-paced Reading.
*Journal of Memory and Language*, 47(4):530–547.

Peter D Turney. 2012. Domain and Function: A Dual-space Model of Semantic Relations and Compositions. Journal of Artificial Intelligence Research, 44:533-585.

Peter D Turney and Patrick Pantel. 2010. From Fre-
quency to Meaning: Vector Space Models of Se-
mantics. Journal of Artificial Intelligence Research,
37:141–188.

Heike Wiese and Joan Maling. 2005. Beers, Kaffi, and Schnaps: Different Grammatical Options for Restaurant Talk Coercions in Three Germanic Languages. Journal of Germanic Linguistics, 17(1):1-38.

Wenting Xue, Meichun Liu, and Stephen Politzer-Ahles. 2021. Processing of Complement Coercion With Aspectual Verbs in Mandarin Chinese: Evidence From a Self-Paced Reading Study. Frontiers in Psychology.

Bingyang Ye, Jingxuan Tu, Elisabetta Jezek, and James Pustejovsky. 2022. Interpreting Logical Metonymy through Dense Paraphrasing. In Proceedings of CogSci.

Alessandra Zarcone, Ken McRae, Alessandro Lenci, and Sebastian Padó. 2017. Complement Coercion: The Joint Effects of Type and Typicality. Frontiers in Psychology.

Alessandra Zarcone and Sebastian Padó. 2011. Gener-
alized Event Knowledge in Logical Metonymy Reso-
lution. In Proceedings of CogSci.

Vitalii Zhelezniak, Aleksandar Savkov, April Shen, and Nils Hammerla. 2019. Correlation Coefficients and Semantic Textual Similarity. In Proceedings of NAACL.
Alessandro Lenci. 2008. Distributional Semantics in Linguistic and Cognitive Research. *Italian Journal of Linguistics*, 20(1):1-31.

Alessandro Lenci, Magnus Sahlgren, Patrick Jeuniaux, Amaru Cuba Gyllensten, and Martina Miliani. 2022. A Comparative Evaluation and Analysis of Three Generations of Distributional Semantic Models. *Language Resources and Evaluation*, pages 1-45.

Omer Levy and Yoav Goldberg. 2014. Dependency-based Word Embeddings. In *Proceedings of ACL*.

Godehard Link. 1983. *The Logical Analysis of Plurals and Mass Terms: A Lattice-theoretical Approach*, volume 127. Blackwell Oxford.

Brian McElree, Matthew J Traxler, Martin J Pickering, Rachel E Seely, and Ray Jackendoff. 2001. Reading time evidence for enriched composition. *Cognition*, 78(1):B17-B25.

Tomas Mikolov, Kai Chen, Greg Corrado, and Jef-
frey Dean. 2013. Efficient Estimation of Word
Representations in Vector Space. *arXiv preprint*
*arXiv:1301.3781*.

George A Miller and Walter G Charles. 1991. Contextual Correlates of Semantic Similarity. *Language and Cognitive Processes*, 6(1):1-28.

Kanishka Misra. 2022. minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Models. *arXiv preprint arXiv:2203.13112*.

Joakim Nivre, Marie-Catherine De Marneffe, Filip Gin-
ter, Yoav Goldberg, Jan Hajic, Christopher D Man-
ning, Ryan McDonald, Slav Petrov, Sampo Pyysalo,
and Natalia Silveira. 2016. Universal Dependencies
v1: A Multilingual Treebank Collection. In *Proceed-
ings of LREC*.

Paolo Pedinotti and Alessandro Lenci. 2020. Don't Invite BERT to Drink a Bottle: Modeling the Interpretation of Metonymies Using BERT and Distributional Representations. In Proceedings of COLING.

Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global Vectors for Word Representation. In Proceedings of EMNLP.

Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep Contextualized Word Representations. In Proceedings of NAACL.

Liina Pylkkanen and Brian McElree. 2006. The Syntax-semantics Interface: On-line Composition of Sentence Meaning. *Handbook of Psycholinguistics*, pages 539–579.

Giulia Rambelli, Emmanuele Chersoni, Alessandro Lenci, Philippe Blache, and Chu-Ren Huang. 2020. Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation. In Proceedings of AACL-IJCNLP.

Enrico Santus, Emmanuele Chersoni, Alessandro Lenci, and Philippe Blache. 2017. Measuring Thematic Fit with Distributional Feature Overlap. In Proceedings of EMNLP.

Enrico Santus, Emmanuele Chersoni, Alessandro Lenci, Chu-Ren Huang, and Philippe Blache. 2016a. Testing APsyn against Vector Cosine on Similarity Estimation. In Proceedings of PACLIC.

Enrico Santus, Tin-Shing Chiu, Qin Lu, Alessandro Lenci, and Chu-Ren Huang. 2016b. What a Nerd! Beating Students and Vector Cosine in the ESL and TOEFL Datasets. In Proceedings of LREC.

Enrico Santus, Hongmin Wang, Emmanuele Chersoni, and Yue Zhang. 2018. A Rank-Based Similarity Metric for Word Embeddings. In Proceedings of ACL.

William Timkey and Marten van Schijndel. 2021. All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality. In Proceedings of EMNLP.

Matthew J Traxler, Martin J Pickering, and Brian McEl-
ree. 2002. Coercion in Sentence Processing: Evi-
dence from Eye-movements and Self-paced Reading.
*Journal of Memory and Language*, 47(4):530–547.

Peter D Turney. 2012. Domain and Function: A Dual-space Model of Semantic Relations and Compositions. *Journal of Artificial Intelligence Research*, 44:533–585.

Peter D Turney and Patrick Pantel. 2010. From Fre-
quency to Meaning: Vector Space Models of Se-
mantics. *Journal of Artificial Intelligence Research*,
37:141–188.

Heike Wiese and Joan Maling. 2005. Beers, Kaffi, and Schnaps: Different Grammatical Options for Restaurant Talk Coercions in Three Germanic Languages. *Journal of Germanic Linguistics*, 17(1):1–38.

Wenting Xue, Meichun Liu, and Stephen Politzer-Ahles. 2021. Processing of Complement Coercion With Aspectual Verbs in Mandarin Chinese: Evidence From a Self-Paced Reading Study. *Frontiers in Psychology*.

Bingyang Ye, Jingxuan Tu, Elisabetta Jezek, and James Pustejovsky. 2022. Interpreting Logical Metonymy through Dense Paraphrasing. In Proceedings of CogSci.

Alessandra Zarcone, Ken McRae, Alessandro Lenci, and Sebastian Padó. 2017. Complement Coercion: The Joint Effects of Type and Typicality. *Frontiers in Psychology*.

Alessandra Zarcone and Sebastian Padó. 2011. Generalized Event Knowledge in Logical Metonymy Resolution. In Proceedings of CogSci.

Vitalii Zhelezniak, Aleksandar Savkov, April Shen, and Nils Hammerla. 2019. Correlation Coefficients and Semantic Textual Similarity. In Proceedings of NAACL.
# A Frame-Based Model of Inherent Polysemy, Copredication and Argument Coercion

Long Chen and Laura Kallmeyer and Rainer Osswald

Heinrich Heine University Düsseldorf
Germany

{chen.long,laura.kallmeyer,rainer.osswald}@hhu.de

## Abstract

The paper presents a frame-based model of inherently polysemous nouns (such as ‘book’, which denotes both a physical object and an informational content) in which the meaning facets are directly accessible via attributes and which also takes into account the semantic relations between the facets. Predication over meaning facets (as in ‘memorize the book’) is then modeled as targeting the value of the corresponding facet attribute while coercion (as in ‘finish the book’) is modeled via specific patterns that enrich the predication. We use a compositional framework whose basic components are lexicalized syntactic trees paired with semantic frames and in which frame unification is triggered by tree composition. The approach is applied to a variety of combinations of predications over meaning facets and coercions.

## 1 Introduction

The lexical representation of inherently polysemous nouns and the variable evocation of their meaning facets by the predications in which they occur continue to be topics of ongoing research. Two questions are of particular interest in this context: (i) What are the mechanisms that underlie *copredication constructions* in which two or more predicates that aim at different meaning facets are applied to the same nominal argument? (ii) How does facet selection for inherently polysemous nouns differ from cases of *argument coercion* in which an apparent mismatch between the semantic type of an argument expression and the requirements of the predicate is resolved by an extended mode of composition that draws on additional pieces of lexical or contextual information?

Examples of inherent polysemy classes are given by nouns such as ‘book’ and ‘letter’, which have a physical facet and an information facet, and by nouns such as ‘documentation’ and ‘classification’, which have a process and a result facet. The sentence in (1a) illustrates a verb-verb copredication

construction in which ‘memorize’ addresses the information facet of the letter while ‘burn’ targets its physical facet.

(1) a. Before leaving Rome he had *memorized* and *burned* a nine-page letter from Moscow.<fnref n="1" />
b. [...] and she *ripped* the *offending letter* to shreds.<fnref n="2" />
c. Although Kafur *burned* the *poem* without having read it, [...]<fnref n="3" />

In verb-adjective copredication constructions like (1b), it is the argument-taking verb and the modifying adjective that address different facets of the noun. The example in (1c), by contrast, would count as a case of coercion if we assume that poems do not inherently come with physical facets.

The examples in (2), retrieved via Sketch Engine's interface to the ACL Anthology Reference Corpus (Bird et al., 2008), show an analogous pattern with respect to the evocation of process and result facets. In (2a), ‘conducted’ addresses the process facets of the classifications while ‘evaluated’ (most probably) refers to their result facets. Likewise, the modifying adjective ‘correct’ targets the result facet in (2b).

(2) a. [...] all *classifications* are *conducted* and *evaluated* on the basis of individual instances.<fnref n="4" />
b. [...] while still *performing correct classification*.<fnref n="5" />
c. The model generates *automatic summaries* of topics [...]<fnref n="6" />

<footnote n="1">¹Forsyth, 1996: *Icon*. (Google Books)</footnote>

<footnote n="2">²Marshall, 2013: *Margaret Fuller - A New American Life*. (Google books)</footnote>

<footnote n="3">³Larkin, 2012: *Al-Mutanabbi*. (Google Books)</footnote>

<footnote n="4">⁴Feng & Hirst, 2012: *Text-level discourse parsing with rich linguistic features*.</footnote>

<footnote n="5">⁵Goldstein & Uzuner, 2010. *Does negation really matter?*</footnote>

<footnote n="6">⁶Ramage et al., 2009: *Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora*.</footnote>
# A Frame-Based Model of Inherent Polysemy, Copredication and Argument Coercion

Long Chen and Laura Kallmeyer and Rainer Osswald

Heinrich Heine University Düsseldorf
Germany

{chen.long,laura.kallmeyer,rainer.osswald}@hhu.de

## Abstract

The paper presents a frame-based model of inherently polysemous nouns (such as ‘book’, which denotes both a physical object and an informational content) in which the meaning facets are directly accessible via attributes and which also takes into account the semantic relations between the facets. Predication over meaning facets (as in ‘memorize the book’) is then modeled as targeting the value of the corresponding facet attribute while coercion (as in ‘finish the book’) is modeled via specific patterns that enrich the predication. We use a compositional framework whose basic components are lexicalized syntactic trees paired with semantic frames and in which frame unification is triggered by tree composition. The approach is applied to a variety of combinations of predications over meaning facets and coercions.

## 1 Introduction

The lexical representation of inherently polysemous nouns and the variable evocation of their meaning facets by the predications in which they occur continue to be topics of ongoing research. Two questions are of particular interest in this context: (i) What are the mechanisms that underlie *copredication constructions* in which two or more predicates that aim at different meaning facets are applied to the same nominal argument? (ii) How does facet selection for inherently polysemous nouns differ from cases of *argument coercion* in which an apparent mismatch between the semantic type of an argument expression and the requirements of the predicate is resolved by an extended mode of composition that draws on additional pieces of lexical or contextual information?

Examples of inherent polysemy classes are given by nouns such as ‘book’ and ‘letter’, which have a physical facet and an information facet, and by nouns such as ‘documentation’ and ‘classification’, which have a process and a result facet. The sentence in (1a) illustrates a verb-verb copredication

construction in which ‘memorize’ addresses the information facet of the letter while ‘burn’ targets its physical facet.

(1) a. Before leaving Rome he had *memorized* and *burned* a nine-page letter from Moscow.<fnref n="1" />
b. [...] and she *ripped* the *offending letter* to shreds.<fnref n="2" />
c. Although Kafur *burned* the *poem* without having read it, [...]<fnref n="3" />

In verb-adjective copredication constructions like (1b), it is the argument-taking verb and the modifying adjective that address different facets of the noun. The example in (1c), by contrast, would count as a case of coercion if we assume that poems do not inherently come with physical facets.

The examples in (2), retrieved via Sketch Engine's interface to the ACL Anthology Reference Corpus (Bird et al., 2008), show an analogous pattern with respect to the evocation of process and result facets. In (2a), ‘conducted’ addresses the process facets of the classifications while ‘evaluated’ (most probably) refers to their result facets. Likewise, the modifying adjective ‘correct’ targets the result facet in (2b).

(2) a. [...] all *classifications* are *conducted* and *evaluated* on the basis of individual instances.<fnref n="4" />
b. [...] while still *performing correct classification*.<fnref n="5" />
c. The model generates *automatic summaries* of topics [...]<fnref n="6" />

<footnote n="1">¹Forsyth, 1996: *Icon*. (Google Books)</footnote>

<footnote n="2">²Marshall, 2013: *Margaret Fuller - A New American Life*. (Google books)</footnote>

<footnote n="3">³Larkin, 2012: *Al-Mutanabbi*. (Google Books)</footnote>

<footnote n="4">⁴Feng & Hirst, 2012: *Text-level discourse parsing with rich linguistic features*.</footnote>

<footnote n="5">⁵Goldstein & Uzuner, 2010. *Does negation really matter?*</footnote>

<footnote n="6">⁶Ramage et al., 2009: *Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora*.</footnote>
The example in (2c), on the other hand, would count
again as a coercion since the noun ‘summary’ (in
contrast to ‘summarization’) does not lexically pro-
vide reference to a process or event. For instance,
‘summary’ does not combine well with verbs like
‘perform’ nor does it go together with ‘process’ in
noun compounds (*‘summary process’ vs. ‘summa-
rization process’).

A more systematic approach to distinguishing co-
ercion from polysemy would draw on empirical data
from corpus studies and psycholinguistic experi-
ments. As to the former type of approach, Jezek and
Vieu (2014) argue that inherent polysemy can be
distinguished from coercion by looking at the vari-
ability of the co-occuring predicates in copredica-
tion constructions, where high variability is taken as
an indicator of polysemy. From a psycholinguistic
point of view, the hypothesis is that complement co-
ercion comes with higher processing costs (Traxler
et al., 2002) than just selecting a lexically provided
facet of a polysemous noun. (See Murphy (2021)
for a more recent overview of the relevant experi-
ments.) The primary goal of the present paper is
not so much to provide a strong empirical basis for
the distinction in question but to introduce a formal
cognitive model that allows us to represent the pos-
tulated semantic differences in a sufficiently fine-
grained way.

A good part of the more recent formal modeling
approaches for inherent polysemy and coercion rely
on some sort of advanced type-theoretical frame-
work such as Type Composition Logic (Asher,
2011), Type Theory with Records (Cooper, 2011),
Montagovian Generative Lexical Theory (Mery
and Retoré, 2015), Unifying Theory of dependent
Types (Chatzikyriakidis and Luo, 2015), and De-
pendent Type Semantics (Kinoshita et al., 2017,
2018). Most if not all of them were at least partly
driven by the aim to overcome what was seen as
formal limitations of Pustejovsky (1995)'s original
proposal. Notably Asher (2011, p. 87) regards the
typed feature structure formalism used by Puste-
jovsky and feature structure unification as inade-
quate for modeling copredication and coercion.

In this paper, we present a *frame-semantic* model
of inherent polysemy and argument coercion. A
central assumption of frame semantics is that *at-
tributes (features)* play a crucial role for the struc-
tured representation of meaning (Barsalou, 1992;
Löbner, 2014). Our approach builds on the frame-
work of Kallmeyer and Osswald (2013), where

(i) frames are defined as *generalized typed feature structures*, (ii) semantic frames are linked with syntactic trees, and (iii) frame unification is guided by syntactic tree operations; cf. Section 2 for details.

Babonnaud et al. (2016) describe a first applica-
tion of this framework to the analysis of polysemy
and coercion. They pursue an “eliminative” strategy
with respect to complex types and objects in that
they avoid positing special “dot objects” that jointly
represent the different meaning facets of an inher-
ently polysemous noun. Their idea is that a frame-
based representation of the facets and the semantic
relations between them is sufficient for explaining
the flexible behavior of polysemous nouns. For in-
stance, they do not introduce a “dot type” *physical-
object* • *information* (*phys-obj* • *info*, for short) for
characterizing the class of polysemous nouns com-
prising ‘book’, ‘letter’, etc. Instead, these nouns
are lexically classified as denoting entities of type
*info(rmation)-carrier*, which is introduced as a sub-
type of *phys-obj* together with the constraint that
its instances have an attribute CONT(ENT) whose
value is of type *info*. More succinctly, in the for-
mal frame description language used in the present
paper: *info-carrier* ⇒ *phys-obj* ∧ CONT : *info*.

Babonnaud et al.'s eliminative strategy has the
following two issues: (i) It is usually necessary to de-
cide on a "primary" facet of which the other facet is
value of an attribute. (ii) In order to access the "non-
primary" facet of a polysemous noun, the predicate
has to anticipate the underlying attribute structure.
To give an illustration, consider the two predications
'memorized the letter' and 'burned the letter'. If 'let-
ter' has the type *info-carrier* then the NP 'the let-
ter' is compatible with the selectional restrictions of
the PATIENT argument of the verb 'burned', which
requires an argument of type *phys-obj*. Figure 1
sketches how argument substitution would lead to
the integration of the NP argument frame into the
verb frame in this case. (The specific choice of the
syntactic category labels will be explained in Sec-
tion 2.)

Combining ‘the letter’ with ‘memorized’ by NP
substitution, on the other hand, calls for more flex-
ible selectional restrictions on the direct object of
the verb: the object NP can have the type *info* or
the type *info-carrier*. This is shown in Figure 2
where ‘memorized’ comes with a disjunctive speci-
fication: either the direct object is of type *info* and
can directly provide the THEME or the THEME is the
value of the direct object’s CONT attribute. How-
The example in (2c), on the other hand, would count
again as a coercion since the noun ‘summary’ (in
contrast to ‘summarization’) does not lexically pro-
vide reference to a process or event. For instance,
‘summary’ does not combine well with verbs like
‘perform’ nor does it go together with ‘process’ in
noun compounds (*‘summary process’ vs. ‘summa-
rization process’).

A more systematic approach to distinguishing co-
ercion from polysemy would draw on empirical data
from corpus studies and psycholinguistic experi-
ments. As to the former type of approach, Jezek and
Vieu (2014) argue that inherent polysemy can be
distinguished from coercion by looking at the vari-
ability of the co-occuring predicates in copredica-
tion constructions, where high variability is taken as
an indicator of polysemy. From a psycholinguistic
point of view, the hypothesis is that complement co-
ercion comes with higher processing costs (Traxler
et al., 2002) than just selecting a lexically provided
facet of a polysemous noun. (See Murphy (2021)
for a more recent overview of the relevant experi-
ments.) The primary goal of the present paper is
not so much to provide a strong empirical basis for
the distinction in question but to introduce a formal
cognitive model that allows us to represent the pos-
tulated semantic differences in a sufficiently fine-
grained way.

A good part of the more recent formal modeling
approaches for inherent polysemy and coercion rely
on some sort of advanced type-theoretical frame-
work such as Type Composition Logic (Asher,
2011), Type Theory with Records (Cooper, 2011),
Montagovian Generative Lexical Theory (Mery
and Retoré, 2015), Unifying Theory of dependent
Types (Chatzikyriakidis and Luo, 2015), and De-
pendent Type Semantics (Kinoshita et al., 2017,
2018). Most if not all of them were at least partly
driven by the aim to overcome what was seen as
formal limitations of Pustejovsky (1995)'s original
proposal. Notably Asher (2011, p. 87) regards the
typed feature structure formalism used by Puste-
jovsky and feature structure unification as inade-
quate for modeling copredication and coercion.

In this paper, we present a *frame-semantic* model
of inherent polysemy and argument coercion. A
central assumption of frame semantics is that *at-
tributes (features)* play a crucial role for the struc-
tured representation of meaning (Barsalou, 1992;
Löbner, 2014). Our approach builds on the frame-
work of Kallmeyer and Osswald (2013), where

(i) frames are defined as *generalized typed feature structures*, (ii) semantic frames are linked with syntactic trees, and (iii) frame unification is guided by syntactic tree operations; cf. Section 2 for details.

Babonnaud et al. (2016) describe a first applica-
tion of this framework to the analysis of polysemy
and coercion. They pursue an “eliminative” strategy
with respect to complex types and objects in that
they avoid positing special “dot objects” that jointly
represent the different meaning facets of an inher-
ently polysemous noun. Their idea is that a frame-
based representation of the facets and the semantic
relations between them is sufficient for explaining
the flexible behavior of polysemous nouns. For in-
stance, they do not introduce a “dot type” *physical-
object* • *information* (*phys-obj* • *info*, for short) for
characterizing the class of polysemous nouns com-
prising ‘book’, ‘letter’, etc. Instead, these nouns
are lexically classified as denoting entities of type
*info(rmation)-carrier*, which is introduced as a sub-
type of *phys-obj* together with the constraint that
its instances have an attribute CONT(ENT) whose
value is of type *info*. More succinctly, in the for-
mal frame description language used in the present
paper: *info-carrier* ⇒ *phys-obj* ∧ CONT : *info*.

Babonnaud et al.’s eliminative strategy has the
following two issues: (i) It is usually necessary to de-
cide on a “primary” facet of which the other facet is
value of an attribute. (ii) In order to access the “non-
primary” facet of a polysemous noun, the predicate
has to anticipate the underlying attribute structure.
To give an illustration, consider the two predications
‘memorized the letter’ and ‘burned the letter’. If ‘let-
ter’ has the type *info-carrier* then the NP ‘the let-
ter’ is compatible with the selectional restrictions of
the PATIENT argument of the verb ‘burned’, which
requires an argument of type *phys-obj*. Figure 1
sketches how argument substitution would lead to
the integration of the NP argument frame into the
verb frame in this case. (The specific choice of the
syntactic category labels will be explained in Sec-
tion 2.)

Combining ‘the letter’ with ‘memorized’ by NP
substitution, on the other hand, calls for more flex-
ible selectional restrictions on the direct object of
the verb: the object NP can have the type *info* or
the type *info-carrier*. This is shown in Figure 2
where ‘memorized’ comes with a disjunctive speci-
fication: either the direct object is of type *info* and
can directly provide the THEME or the THEME is the
value of the direct object’s CONT attribute. How-
<figure><img src="image_20.png" /><figcaption>Figure 1: Syntactic and semantic composition for ‘burned the letter’ (preliminary version)</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>Figure 2: Lexicalized construction for ‘memorized’ along the lines of Babonnaud et al. (2016)</figcaption></figure>

ever, since having different meaning facets is a lexical property of the polysemous noun, access to them should be provided by the noun entry as well.

A related issue is that Babonnaud et al. (2016) employ the same kind of disjunctive encoding for modeling coercion. However, there should be a distinction between coercion mechanisms (linked to the predicate) and accessing different meaning facets (provided by the noun). An example where the difference matters is the selection of the object of ‘perform’. Assuming that in ‘perform an annotation’, the frame of ‘perform’ picks the creation event of the ‘annotation’ frame, and assuming that such an event is also present in the frame of ‘summary’, it is not clear why the latter cannot be targeted by ‘perform’. The crucial difference is that in “perform an annotation” a meaning facet is targeted (‘annotation’ is a process • result dot type noun), while “perform a summary” requires a coercion. The unavailability of ‘perform a summary’ might therefore be due to the absence of an event facet for ‘summary’ and, furthermore, the unavailability of an adequate coercion pattern for ‘perform’.

The goal of the present paper is to show how a non-eliminative strategy for modeling polysemous nouns by means of frames can overcome the described issues. To this end, dot types are added as proper members of the type hierarchy. They are not related to their component types by inher-

<figure><img src="image_22.png" /><figcaption>Figure 3: Eliminative (a) and non-eliminative (b) frame representations for inherently polysemous nouns like ‘letter’ and ‘book’</figcaption></figure>

itance but rather by functional relations, i.e., by attributes. For example, instances of type *phys-obj•info* have two attributes *OBJ(ECT)-FACET* and *INFO(RMATION)-FACET* whose values are of type *info-carrier* and *info*, respectively. The two facets are related by the CONT attribute as before:

$$ (3) \quad \begin{array}{l} \texttt{phys-obj•info} \Rightarrow \texttt{OBJ-FACET: info-carrier} \wedge \\ \quad \texttt{INFO-FACET: info} \wedge \\ \quad \texttt{OBJ-FACET} \cdot \texttt{CONT} \doteq \texttt{INFO-FACET} \end{array} $$

Figure 3 depicts the two frame representations for the eliminative and the non-eliminative strategy side by side. Argument coercion, on the other hand, does not involve facet selection but is analyzed via additional patterns that can be used to augment the semantics of a predicate.

## 2 Background: Syntax-driven frame composition

The formalization of frame semantics used in the present paper is a slightly modified version of the one proposed by Kallmeyer and Osswald (2013). Frames are understood as *generalized feature structures*. Instead of requiring a distinguished root node from which every other node is accessible via a finite attribute sequence, the generalized version allows for multiple labeled nodes under the condition that each node is accessible from at least one of the labeled nodes. Correspondingly, frame unification does not require the identification of designated root nodes but relies on the identification of nodes with the same label. Frames can be defined as *minimal models* of conjunctive attribute-value formulas. The underlying logic is described in Appendix A.

Kallmeyer and Osswald (2013) combine frame semantics with Tree Adjoining Grammars (TAG). The basic components of this approach are *elementary constructions*, which are pairs of elementary syntactic trees and semantic frames where (some of) the constituent nodes of the tree are linked to nodes of the frame. The link is encoded by an I(NDEX)
<figure><img src="image_20.png" /><figcaption>Figure 1: Syntactic and semantic composition for ‘burned the letter’ (preliminary version)</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>Figure 2: Lexicalized construction for ‘memorized’ along the lines of Babonnaud et al. (2016)</figcaption></figure>

ever, since having different meaning facets is a lexical property of the polysemous noun, access to them should be provided by the noun entry as well.

A related issue is that Babonnaud et al. (2016) employ the same kind of disjunctive encoding for modeling coercion. However, there should be a distinction between coercion mechanisms (linked to the predicate) and accessing different meaning facets (provided by the noun). An example where the difference matters is the selection of the object of ‘perform’. Assuming that in ‘perform an annotation’, the frame of ‘perform’ picks the creation event of the ‘annotation’ frame, and assuming that such an event is also present in the frame of ‘summary’, it is not clear why the latter cannot be targeted by ‘perform’. The crucial difference is that in “perform an annotation” a meaning facet is targeted (‘annotation’ is a process • result dot type noun), while “perform a summary” requires a coercion. The unavailability of ‘perform a summary’ might therefore be due to the absence of an event facet for ‘summary’ and, furthermore, the unavailability of an adequate coercion pattern for ‘perform’.

The goal of the present paper is to show how a non-eliminative strategy for modeling polysemous nouns by means of frames can overcome the described issues. To this end, dot types are added as proper members of the type hierarchy. They are not related to their component types by inher-

<figure><img src="image_22.png" /><figcaption>Figure 3: Eliminative (a) and non-eliminative (b) frame representations for inherently polysemous nouns like ‘letter’ and ‘book’</figcaption></figure>

itance but rather by functional relations, i.e., by attributes. For example, instances of type *phys-obj•info* have two attributes *OBJ(ECT)-FACET* and *INFO(RMATION)-FACET* whose values are of type *info-carrier* and *info*, respectively. The two facets are related by the CONT attribute as before:

$$ (3) \quad \begin{array}{l} \texttt{phys-obj•info} \Rightarrow \texttt{OBJ-FACET: info-carrier} \wedge \\ \quad \texttt{INFO-FACET: info} \wedge \\ \quad \texttt{OBJ-FACET} \cdot \texttt{CONT} \doteq \texttt{INFO-FACET} \end{array} $$

Figure 3 depicts the two frame representations for the eliminative and the non-eliminative strategy side by side. Argument coercion, on the other hand, does not involve facet selection but is analyzed via additional patterns that can be used to augment the semantics of a predicate.

## 2 Background: Syntax-driven frame composition

The formalization of frame semantics used in the present paper is a slightly modified version of the one proposed by Kallmeyer and Osswald (2013). Frames are understood as *generalized feature structures*. Instead of requiring a distinguished root node from which every other node is accessible via a finite attribute sequence, the generalized version allows for multiple labeled nodes under the condition that each node is accessible from at least one of the labeled nodes. Correspondingly, frame unification does not require the identification of designated root nodes but relies on the identification of nodes with the same label. Frames can be defined as *minimal models* of conjunctive attribute-value formulas. The underlying logic is described in Appendix A.

Kallmeyer and Osswald (2013) combine frame semantics with Tree Adjoining Grammars (TAG). The basic components of this approach are *elementary constructions*, which are pairs of elementary syntactic trees and semantic frames where (some of) the constituent nodes of the tree are linked to nodes of the frame. The link is encoded by an I(NDEX)
Constraints for dot types and their meaning facets:

`phys-obj • info` ⇒ OBJ-FACET: `info-carrier` ∧ INFO-FACET: `info` ∧ OBJ-FACET • CONT = INFO-FACET
`process • result` ⇒ EVENT-FACET: `process` ∧ EVENT-FACET • RESULT • CREATION = EVENT-FACET
`process • result` ∧ EVENT-FACET • RESULT • OBJ-FACET ⇒ EVENT-FACET • RESULT • OBJ-FACET = OBJ-FACET
`process • result` ∧ EVENT-FACET • RESULT • INFO-FACET ⇒ EVENT-FACET • RESULT • INFO-FACET = INFO-FACET

Constraints for single types and their meaning facets:

`phys-obj` ⇒ OBJ-FACET = SELF
`event` ⇒ EVENT-FACET = SELF
`info` ⇒ INFO-FACET = SELF

### Subtype constraints:

`info-carrier` ⇒ `phys-obj`

`sheet` ⇒ `phys-obj`

`poem` ⇒ `info`

`book` ⇒ `phys-obj • info`

`letter` ⇒ `phys-obj • info`

`summary` ⇒ `phys-obj • info`

`annotation` ⇒ `process • result`

`classification` ⇒ `process • result`

`process` ⇒ `event`

### Specification of attributes depending on types:

`annotation` ⇒ EVENT-FACET • RESULT: `phys-obj • info`

`classification` ⇒ EVENT-FACET • RESULT: `phys-obj • info`

### Type incompatibilites:

`phys-obj` ∧ `info` ⇒ ⊥

`phys-obj` ∧ `event` ⇒ ⊥

`event` ∧ `info` ⇒ ⊥

### Type-attribute incompatibilites:

`phys-obj` ∧ INFO-FACET: `⊤` ⇒ ⊥

`event` ∧ INFO-FACET: `⊤` ⇒ ⊥

`info` ∧ EVENT-FACET: `⊤` ⇒ ⊥

`phys-obj` ∧ EVENT-FACET: `⊤` ⇒ ⊥

`event` ∧ OBJ-FACET: `⊤` ⇒ ⊥

`info` ∧ OBJ-FACET: `⊤` ⇒ ⊥

<caption>Figure 4: Selected universal AV constraints</caption>

feature at the constituent nodes. Tree composition then gives rise to the identification of index values and, thereby, to specific constraints on how the associated semantic frames are unified. For example, the composition of the two constructions in Figure 1 leads to the identification of y and z, i.e. $y = z$.

The syntactic side of the approach is not restricted to TAG but generalizes to other tree rewriting formalisms. In this paper, we use the formalism of Tree Wrapping Grammars (TWG) together with grammatical concepts of Role and Reference Grammar (RRG; Van Valin 2005), for which TWG has been developed (Kallmeyer et al., 2013).

RRG provides an elaborate theory of clause linkage, which comes in handy for the analysis of copredication constructions, among others. Instead of an X-bar scheme, RRG assumes a layered structure consisting of *nucleus*, *core* and *clause*. The nucleus contains the main predicate, the core contains the nucleus and the (non-extracted) syntactic arguments, and the clause includes the core and extracted arguments. Each layer can have a *periphery* of adjuncts. Grammatical operators, that is, closed-class elements encoding tense, modality, aspect, etc., attach to different layers depending on their scope.

Concerning complex constructions, RRG draws not only a distinction between coordination and subordination but assumes in addition *cosubordination* constructions, which are dependent but non-embedded structures of the general form

```[ ]]x [ ]]x]x.
```In such constructions, operators that apply to category X are usually realized only once but have scope over both X-daughters.

The tree composition operations of TWG are (simple) substitution (replacing a non-terminal leaf by a tree, as in Figure 1), sister adjunction (adding a tree as a subtree of a non-leaf, see the adjunction of ‘and’ in Figure 9) and wrapping substitution (splitting the new tree at a dominance-edge, filling a substitution node with the lower part and adding the upper part to the root of the target tree, cf. Figure 9).

## 3 Predications over meaning facets

In this section, we develop an analysis of predications that target existing meaning facets (either of dot type nouns or of single type nouns).

**Universal constraints.** As already mentioned, we introduce attributes for meaning facets and specific types for dot types. Meaning facets occur systematically for certain types, and they are therefore introduced by universal attribute-value constraints of the form $\varphi \Rightarrow \psi$. (Cf. the appendix for the formal background). Some of the relevant constraints for dot types `phys-obj • info` and `process • result` are given in Figure 4. They specify available meaning facets together with the specific relations that hold between the different facets: For `phys-obj • info`, the CONT value of the OBJ-FACET is the INFO-FACET, while for `process • result`, the CREATION value of the OBJ-FACET is the EVENT-FACET and the RESULT of the EVENT-FACET is the OBJ-FACET. For sin-
<figure><img src="image_23.png" /><figcaption>Figure 4: Selected universal AV constraints</figcaption></figure>

feature at the constituent nodes. Tree composition then gives rise to the identification of index values and, thereby, to specific constraints on how the associated semantic frames are unified. For example, the composition of the two constructions in Figure 1 leads to the identification of y and z, i.e. $y \doteq z$.

The syntactic side of the approach is not restricted to TAG but generalizes to other tree rewriting formalisms. In this paper, we use the formalism of Tree Wrapping Grammars (TWG) together with grammatical concepts of Role and Reference Grammar (RRG; Van Valin 2005), for which TWG has been developed (Kallmeyer et al., 2013).

RRG provides an elaborate theory of clause linkage, which comes in handy for the analysis of copredication constructions, among others. Instead of an X-bar scheme, RRG assumes a layered structure consisting of *nucleus*, *core* and *clause*. The nucleus contains the main predicate, the core contains the nucleus and the (non-extracted) syntactic arguments, and the clause includes the core and extracted arguments. Each layer can have a *periphery* of adjuncts. Grammatical operators, that is, closed-class elements encoding tense, modality, aspect, etc., attach to different layers depending on their scope.

Concerning complex constructions, RRG draws not only a distinction between coordination and subordination but assumes in addition *cosubordination* constructions, which are dependent but non-embedded structures of the general form

```[ ]x [ ]x]x.
``` In such constructions, operators that apply to category X are usually realized only once but have scope over both X-daughters.

The tree composition operations of TWG are (simple) substitution (replacing a non-terminal leaf by a tree, as in Figure 1), sister adjunction (adding a tree as a subtree of a non-leaf, see the adjunction of ‘and’ in Figure 9) and wrapping substitution (splitting the new tree at a dominance-edge, filling a substitution node with the lower part and adding the upper part to the root of the target tree, cf. Figure 9).

## 3 Predications over meaning facets

In this section, we develop an analysis of predications that target existing meaning facets (either of dot type nouns or of single type nouns).

**Universal constraints.** As already mentioned, we introduce attributes for meaning facets and specific types for dot types. Meaning facets occur systematically for certain types, and they are therefore introduced by universal attribute-value constraints of the form $\varphi \Rightarrow \psi$. (Cf. the appendix for the formal background). Some of the relevant constraints for dot types *phys-obj • info* and *process • result* are given in Figure 4. They specify available meaning facets together with the specific relations that hold between the different facets: For *phys-obj • info*, the CONT value of the OBJ-FACET is the INFO-FACET, while for *process • result*, the CREATION value of the OBJ-FACET is the EVENT-FACET and the RESULT of the EVENT-FACET is the OBJ-FACET. For sin-
<figure><img src="image_23.png" /><figcaption>Figure 5: Application of universal constraints to the lexical entry of *letter*</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>Figure 6: Revised syntactic and semantic composition for ‘burned the letter’ (cf. Figure 1)</figcaption></figure>

single types, the frame node is its own corresponding meaning facet (with SELF denoting the identity function properly restricted). The third group of constraints specifies subtype relations, for instance every entity of type *book* is also of type *phys-obj* • *info*, and the last two groups detail incompatibilities between types (e.g., nothing can be of types *event* and *phys-obj* at the same time) and between types and attributes (e.g., nothing can be of type *phys-obj* while having an EVENT-FACET). Figure 5 shows the application of these constraints to the lexical frame of ‘letter’.

**Single verbal predications.** (4) lists cases of verbs predicating over single meaning facets of their object nouns (the latter being single or dot type nouns).

(4) a. Kim burned the sheet/letter.

b. Kim memorized the poem/letter.

c. Kim performed the dance/annotation.

d. Kim evaluated the essay/annotation.

The elementary constructions for the verbs describe the event and its participants and determine which facet of the respective arguments fills which semantic role; see the entry for ‘burned’ in Figure 6. In combination with the I features on the nodes, the frame encodes that the OBJ-FACET of the di-

<figure><img src="image_25.png" /><figcaption>Figure 7: Resulting frame for ‘burned the letter’</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>Figure 8: Tree frame pairs for ‘correct’ and ‘automatic’</figcaption></figure>

rect object is the PATIENT of the *burning* event. The derivation in Figure 6 leads to the frame in Figure 7. The constructions for ‘memorized’, ‘performed’ and ‘evaluated’ look similar, except that the THEME expressed by the direct object is provided by different facets. Due to the frame constraints for meaning facets in single type nouns, the analysis works there as well (as in ‘burned the sheet’).

**Adjectival predications.** A second type of predication we consider in this paper are adjectival predications as in (5) where two adjectives modify a noun while targeting different facets of it.

(5) a. Where manual fine-grained annotation is unavailable, [...]<fnref n="7" />

b. the correct automatic annotation

The elementary constructions for ‘correct’ and ‘automatic’ in (5b) are given in Figure 8. The trees are added by sister adjunction to the CORE<sub>N</sub> node of an NP tree. The CORE<sub>N</sub> is the immediate daughter of NP, and both constituent nodes carry the same I feature. This way, the frame of the adjective unifies with the noun frame.

**Multi-verb copredication.** We now consider constructions of the form ‘NP V₁ and V₂ NP’, as in (6).

(6) a. Kim memorized and burned the letter.

b. Kim performed and evaluated the annotation.

The two verbs in (6a) and (6b) share their arguments while targeting different facets of the object NP and assigning different semantic roles. They

<footnote n="7">⁷Abney and Bird, 2010. *The Human Language Project: Building a Universal Corpus of the World's Languages.*</footnote>
<figure><img src="image_24.png" /><figcaption>Figure 5: Application of universal constraints to the lexical entry of *letter*</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>Figure 6: Revised syntactic and semantic composition for ‘burned the letter’ (cf. Figure 1)</figcaption></figure>

single types, the frame node is its own corresponding meaning facet (with SELF denoting the identity function properly restricted). The third group of constraints specifies subtype relations, for instance every entity of type *book* is also of type *phys-obj* • *info*, and the last two groups detail incompatibilities between types (e.g., nothing can be of types *event* and *phys-obj* at the same time) and between types and attributes (e.g., nothing can be of type *phys-obj* while having an EVENT-FACET). Figure 5 shows the application of these constraints to the lexical frame of ‘letter’.

**Single verbal predications.** (4) lists cases of verbs predicating over single meaning facets of their object nouns (the latter being single or dot type nouns).

(4) a. Kim burned the sheet/letter.

b. Kim memorized the poem/letter.

c. Kim performed the dance/annotation.

d. Kim evaluated the essay/annotation.

The elementary constructions for the verbs describe the event and its participants and determine which facet of the respective arguments fills which semantic role; see the entry for ‘burned’ in Figure 6. In combination with the I features on the nodes, the frame encodes that the OBJ-FACET of the di-

<figure><img src="image_26.png" /><figcaption>Figure 7: Resulting frame for ‘burned the letter’</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>Figure 8: Tree frame pairs for ‘correct’ and ‘automatic’</figcaption></figure>

rect object is the PATIENT of the *burning* event. The derivation in Figure 6 leads to the frame in Figure 7. The constructions for ‘memorized’, ‘performed’ and ‘evaluated’ look similar, except that the THEME expressed by the direct object is provided by different facets. Due to the frame constraints for meaning facets in single type nouns, the analysis works there as well (as in ‘burned the sheet’).

**Adjectival predications.** A second type of predication we consider in this paper are adjectival predications as in (5) where two adjectives modify a noun while targeting different facets of it.

(5) a. Where manual fine-grained annotation is unavailable, [...]<fnref n="7" />

b. the correct automatic annotation

The elementary constructions for ‘correct’ and ‘automatic’ in (5b) are given in Figure 8. The trees are added by sister adjunction to the CORE<sub>N</sub> node of an NP tree. The CORE<sub>N</sub> is the immediate daughter of NP, and both constituent nodes carry the same I feature. This way, the frame of the adjective unifies with the noun frame.

**Multi-verb copredication.** We now consider constructions of the form ‘NP V₁ and V₂ NP’, as in (6).

(6) a. Kim memorized and burned the letter.

b. Kim performed and evaluated the annotation.

The two verbs in (6a) and (6b) share their arguments while targeting different facets of the object NP and assigning different semantic roles. They

<footnote n="7">⁷Abney and Bird, 2010. *The Human Language Project: Building a Universal Corpus of the World's Languages.*</footnote>
<figure><img src="image_27.png" /><figcaption>Figure 9: Derivation for ‘memorized and burned”</figcaption></figure>

constitute a complex event with a certain temporal structure, but they are still distinguishable within the complex event. We therefore analyze their syntactic structure as constituting a complex CORE consisting of two single COREs, i.e., a CORE co-subordination construction in RRG terms; see the tree in Figure 10. We capture the information about the shared subject by means of the features CONTROLLER (CTRL; for the CORE that provides the subject) and PIVOT (for the CORE that needs to retrieve an argument). The shared object NP, however, is present in the trees of both verbs, and the two NP nodes are merged via wrapping substitution (see Figure 9). The first CORE contains a dominance edge that leaves room between the upper CORE node and the shared object NP for inserting the second CORE while merging the object NP nodes.

We introduce a frame type *comp(lex)-event* for events that have several component events that all stand in a *part-of* relation to it. This relation between the complex event and its parts is specified in the corresponding unanchored elementary tree frame pairs, i.e., in the construction.

Figure 9 gives the tree-frame pairs for the two verbs. Each contributes an event that is part of a joint complex event. The first tree contributes the subject or, in more general RRG notions, the privileged syntactic argument (PSA). This is shared between all component events and, to this end, it is made available at the higher CORE node via a CTRL attribute. The second argument of the two trees is a shared NP node, where the sharing is realized via wrapping. Both the PSA and second NP argument can fill different semantic roles for the two events, and, furthermore, different facets of them can fill these roles. As a result of the adjunction, the frames

<figure><img src="image_28.png" /><figcaption>Figure 10: Result of the derivation in Figure 9</figcaption></figure>

$e_0$ and $e_3$ unify, which yields a frame (Figure 10) that expresses that we have a complex event consisting of two part events, *memorizing* and *burning*, and that the subject filler provides the respective agents via its object facet, while the object NP filler provide the *THEME* of *memorizing* via its *INFO-FACET* and the *PATIENT* of *burning* via its *OBJ-FACET*.

For (6b), the analysis is similar, except for targeting different facets of the object NP.

## 4 Coercion

We now extend our analysis to cases of coercion as in (1c) ('burn the poem') and (2c) ('automatic summary'). The examples in (7) show that for the same predicate, depending on the context, different additional frame fragments are coerced, even for the same object nouns.
<figure><img src="image_28.png" /><figcaption>Figure 9: Derivation for ‘memorized and burned’</figcaption></figure>

constitute a complex event with a certain temporal structure, but they are still distinguishable within the complex event. We therefore analyze their syntactic structure as constituting a complex CORE consisting of two single COREs, i.e., a CORE co-subordination construction in RRG terms; see the tree in Figure 10. We capture the information about the shared subject by means of the features CONTROLLER (CTRL; for the CORE that provides the subject) and PIVOT (for the CORE that needs to retrieve an argument). The shared object NP, however, is present in the trees of both verbs, and the two NP nodes are merged via wrapping substitution (see Figure 9). The first CORE contains a dominance edge that leaves room between the upper CORE node and the shared object NP for inserting the second CORE while merging the object NP nodes.

We introduce a frame type *comp(lex)-event* for events that have several component events that all stand in a *part-of* relation to it. This relation between the complex event and its parts is specified in the corresponding unanchored elementary tree frame pairs, i.e., in the construction.

Figure 9 gives the tree-frame pairs for the two verbs. Each contributes an event that is part of a joint complex event. The first tree contributes the subject or, in more general RRG notions, the privileged syntactic argument (PSA). This is shared between all component events and, to this end, it is made available at the higher CORE node via a CTRL attribute. The second argument of the two trees is a shared NP node, where the sharing is realized via wrapping. Both the PSA and second NP argument can fill different semantic roles for the two events, and, furthermore, different facets of them can fill these roles. As a result of the adjunction, the frames

<figure><img src="image_29.png" /><figcaption>Figure 10: Result of the derivation in Figure 9</figcaption></figure>

$e_0$ and $e_3$ unify, which yields a frame (Figure 10) that expresses that we have a complex event consisting of two part events, *memorizing* and *burning*, and that the subject filler provides the respective agents via its object facet, while the object NP filler provide the *THEME* of *memorizing* via its *INFO-FACET* and the *PATIENT* of *burning* via its *OBJ-FACET*.

For (6b), the analysis is similar, except for targeting different facets of the object NP.

## 4 Coercion

We now extend our analysis to cases of coercion as in (1c) ('burn the poem') and (2c) ('automatic summary'). The examples in (7) show that for the same predicate, depending on the context, different additional frame fragments are coerced, even for the same object nouns.
Frame for ‘summary’:

<figure><img src="image_29.png" /><figcaption>Revised frame for ‘automatic’ (tree as in Figure 8):</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>Figure 11: Frames for ‘automatic summary’</figcaption></figure>

(7) a. [...] Mr. Darcy had much better *finish his letter*.<sup>8</sup>

b. [...] once we start to translate words in a zone, we have to *finish all its words* before moving outside again.<sup>9</sup>

Let us start by discussing ‘automatic summary’. If the modified noun has an event facet, we can apply ‘automatic’ directly. Otherwise, we have to coerce an event. This could simply be done by adding an attribute EVENT-FACET on the fly via unification. But this would predict that we target always the same event, which is not the case. The event facet (if existent) is unique, but not the coerced events. For example, let us assume that Kim has to grade a number of manually and automatically produced summaries. In this situation, ‘finished’ in sentence (8) may refer to the process of grading the summary while ‘automatic’ refers to creating it.

(8) Kim just *finished* a bad *automatic summary*.

In ‘automatic summary’, the noun does not have an event facet, while the adjective adds a manner specification to a process, in this case to the creation of the summary, which is part of the frame of ‘summary’ (see Figure 11).<sup>10</sup> This type of coercion, namely targeting the creation event, is common among manner adjectives, so we assume that there is a coercion pattern for this, that can be applied to manner modifications in general. Coercion

<figure><img src="image_31.png" /><figcaption>Figure 12: Tree-frame pair for ‘finished’</figcaption></figure>

patterns are defined as separate classes in the metagrammar (a factorized description of elementary tree frame pairs) and then combined in a disjunction with the basic pattern. Since coercion patterns are metagrammar classes, they can be (re)used in different constructions. Figure 11 shows the resulting disjunction of frames for ‘automatic’. The first frame is used when combining with a noun that has an event facet, while the second is used when combining with a noun that has a CREATION attribute. (Note that, technically, this disjunction is part of the metagrammar and will therefore already be compiled out when computing the elementary constructions.) The composition of ‘automatic summary’ (*x* ≜ *y* in Figure 11) necessarily chooses the coercion option, since a *summary* cannot have an event facet. Coercion can easily be combined with predications over existing meaning facets, as in ‘evaluate the automatic summary’ since the meaning facets of the noun are not changed by the coercion patterns.

As we have seen, ‘automatic’ in ‘automatic summary’ follows an existing path in the noun frame in order to retrieve its argument. This is different for ‘finish’ in (7). When triggering a coercion, ‘finish’ creates a new *event* frame (the coerced event) which embeds the denotation of the noun as a participant, more concretely as an undergoer. This is expressed in the frame in Figure 12 where the disjunction contains the basic pattern (the THEME 2 is the existing EVENT-FACET) and a coercion pattern (2 is a newly created event). An aspect that is missing here is that the coerced event tends to be of a type that corresponds to the telic qualia of the noun (e.g., *writing* in (7a); cf. Pustejovsky 1995). One could model this within frames by including frame types as proper frame objects. We leave this for future research.

With this analysis, we can apply more than one coercion leading to different coerced frame elements of the same type, as in (8). And we can

<footnote n="8"><sup>8</sup>Jane Austen, 1813: Pride and Prejudice.</footnote>

<footnote n="9"><sup>9</sup>Koehn & Haddow, 2009. Analysing the effect of out-of-domain data on SMT systems.</footnote>

<footnote n="10"><sup>10</sup>Our classification of ‘summary’ as a dot object noun may be disputable. However, the coercion mechanism presented here would apply in the same way if ‘summary’ had an INFO-FACET only.</footnote>
Frame for ‘summary’:

<figure><img src="image_30.png" /></figure>

Revised frame for ‘automatic’ (tree as in Figure 8):

<figure><img src="image_31.png" /><figcaption>Figure 11: Frames for ‘automatic summary’</figcaption></figure>

(7) a. [...] Mr. Darcy had much better *finish his letter*.<sup>8</sup>

b. [...] once we start to translate words in a
zone, we have to *finish all its words* before
moving outside again.<sup>9</sup>

Let us start by discussing ‘automatic summary’. If the modified noun has an event facet, we can apply ‘automatic’ directly. Otherwise, we have to coerce an event. This could simply be done by adding an attribute EVENT-FACET on the fly via unification. But this would predict that we target always the same event, which is not the case. The event facet (if existent) is unique, but not the coerced events. For example, let us assume that Kim has to grade a number of manually and automatically produced summaries. In this situation, ‘finished’ in sentence (8) may refer to the process of grading the summary while ‘automatic’ refers to creating it.

(8) Kim just *finished* a bad *automatic summary*.

In ‘automatic summary’, the noun does not have
an event facet, while the adjective adds a manner
specification to a process, in this case to the cre-
ation of the summary, which is part of the frame
of ‘summary’ (see Figure 11).<sup>10</sup> This type of coer-
cion, namely targeting the creation event, is com-
mon among manner adjectives, so we assume that
there is a coercion pattern for this, that can be ap-
plied to manner modifications in general. Coercion

<figure><img src="image_32.png" /><figcaption>Figure 12: Tree-frame pair for ‘finished’</figcaption></figure>

patterns are defined as separate classes in the meta-
grammar (a factorized description of elementary
tree frame pairs) and then combined in a disjunc-
tion with the basic pattern. Since coercion patterns
are metagrammar classes, they can be (re)used in
different constructions. Figure 11 shows the result-
ing disjunction of frames for ‘automatic’. The first
frame is used when combining with a noun that has
an event facet, while the second is used when com-
bining with a noun that has a CREATION attribute.
(Note that, technically, this disjunction is part of the
metagrammar and will therefore already be com-
piled out when computing the elementary construc-
tions.) The composition of ‘automatic summary’
(x ≜ y in Figure 11) necessarily chooses the coer-
cion option, since a *summary* cannot have an event
facet. Coercion can easily be combined with predi-
cations over existing meaning facets, as in ‘evaluate
the automatic summary’ since the meaning facets of
the noun are not changed by the coercion patterns.

As we have seen, ‘automatic’ in ‘automatic sum-
mary’ follows an existing path in the noun frame in
order to retrieve its argument. This is different for
‘finish’ in (7). When triggering a coercion, ‘finish’
creates a new *event* frame (the coerced event) which
embeds the denotation of the noun as a participant,
more concretely as an undergoer. This is expressed
in the frame in Figure 12 where the disjunction con-
tains the basic pattern (the THEME ② is the existing
EVENT-FACET) and a coercion pattern (② is a newly
created event). An aspect that is missing here is that
the coerced event tends to be of a type that corre-
sponds to the telic qualia of the noun (e.g., *writing* in
(7a); cf. Pustejovsky 1995). One could model this
within frames by including frame types as proper
frame objects. We leave this for future research.

With this analysis, we can apply more than one
coercion leading to different coerced frame ele-
ments of the same type, as in (8). And we can

<footnote n="8">⁸Jane Austen, 1813: Pride and Prejudice.</footnote>

<footnote n="9">⁹Koehn & Haddow, 2009. Analysing the effect of out-of-domain data on SMT systems.</footnote>

<footnote n="10">¹⁰Our classification of ‘summary’ as a dot object noun may be disputable. However, the coercion mechanism presented here would apply in the same way if ‘summary’ had an INFO-FACET only.</footnote>
also apply coercion to dot type nouns, creating new
frame nodes in addition to the available facets, even
when one of the facets matches the type require-
ments. For example, if we replace ‘summary’ in (8)
by ‘annotation’ then ‘automatic’ refers to the event
facet of ‘annotation’. In this case, the basic pattern
as well as the coercion pattern are possible.

5 Conclusion

In this paper, we proposed a frame-based analy-
sis of dot objects, predications over their meaning
facets, and, in contrast to this, coercion. A crucial
aspect of our analysis is that the meaning facets are
modeled as attributes in the lexical frames of dot
type nouns, while coercion involves the application
of coercion patterns that are defined in the meta-
grammar. Their application is constrained by lexical
properties, but the meaning components added
by coercion are not part of the lexical entries and
are in particular not meaning facets. This accounts
for the high flexibility of coercion, i.e., the possible
variability of the coerced meaning components.

Acknowledgments

This work has been carried out as part of the re-
search project ‘Coercion and Copredication as Flex-
ible Frame Composition’ funded by DFG (Ger-
man Science Foundation). Parts of the research
were also funded by the European Research Council
(ERC) project TreeGraSP and by the multilingual-
ism part of the European project UNIVERSEH. We
would like to thank the anonymous reviewers for
their valuable comments.

**Appendix: Attribute-value logic of frames**

The appendix describes the attribute-value (AV)
logic that underlies the frame approach of this ar-
ticle; see also Kallmeyer and Osswald (2013), who
in turn build on Hegner (1994). The logic makes
use of two kinds of expressions: *AV formulas* and
*AV descriptions*.

AV descriptions are evaluated at frame
nodes, formulas on whole frames. AV ex-
pressions are defined over a vocabulary
⟨Attr, Typ, Rel, Nnam, Nvar⟩ consisting of a fi-
nite set Attr of attribute symbols, a finite set Typ of
type symbols, a finite set Rel = ⋃<sub>n</sub> Rel<sub>n</sub> of relation
symbols (where Rel<sub>n</sub> are n-ary relation symbols), a
finite set Nnam of node names (or nominals), and a
countably infinite set Nvar of node variables. The
members of Nlab = Nnam ∪ Nvar are referred

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /><figcaption>Figure 13: The middle column shows primitive AV de-
scriptions (a) and formulas (b), their notation as AV ma-
trices (right columns), and the structures they denote (left
columns)</figcaption></figure>

to as *node labels*. The *primitive AV descriptions*
consist of the following expressions:

$$
(9) \quad t \mid p:t \mid p \doteq q \mid \langle p_1, \dots, p_n \rangle : r \mid p \doteq k
$$

with $t \in \text{Typ}$, $r \in \text{Rel}$, $p, q, p_i \in \text{Attr}^*$, and $k \in \text{Nlab}$.
The intended meaning of these expressions is de-
picted in Figure 13a, which also shows the equiva-
lent matrix style notations. The filled circles indi-
cate the nodes at which the expressions are evalu-
ated. Node labels are depicted inside nodes, type
symbols are depicted at the outside of nodes.

The set of *primitive AV formulas* is defined as fol-
lows:

$$
(10) \quad k \cdot p : t \mid k \cdot p \doteq l \cdot q \mid \langle k_1 \cdot p_1, \dots, k_n \cdot p_n \rangle : r
$$

with $t \in \text{Typ}$, $r \in \text{Rel}$, $p, q, p_i \in \text{Attr}^*$, and $k, l, k_i \in \text{Nlab}$.
AV formulas state that there are certain labeled nodes that have certain properties.
The intended meaning of AV formulas is sketched in Figure 13b.

Formally, the satisfaction of AV expressions is
defined relative to a *structure* ⟨V, I, g⟩ over the given
vocabulary consisting of a set V, the *universe* of
“nodes”, an *interpretation function* I defined on
Attr ∪ Typ ∪ Rel ∪ Nnam, and a partial *variable
assignment function* g from Nvar to V. The func-
also apply coercion to dot type nouns, creating new frame nodes in addition to the available facets, even when one of the facets matches the type requirements. For example, if we replace ‘summary’ in (8) by ‘annotation’ then ‘automatic’ refers to the event facet of ‘annotation’. In this case, the basic pattern as well as the coercion pattern are possible.

# 5 Conclusion

In this paper, we proposed a frame-based analysis of dot objects, predications over their meaning facets, and, in contrast to this, coercion. A crucial aspect of our analysis is that the meaning facets are modeled as attributes in the lexical frames of dot type nouns, while coercion involves the application of coercion patterns that are defined in the meta-grammar. Their application is constrained by lexical properties, but the meaning components added by coercion are not part of the lexical entries and are in particular not meaning facets. This accounts for the high flexibility of coercion, i.e., the possible variability of the coerced meaning components.

# Acknowledgments

This work has been carried out as part of the research project ‘Coercion and Copredication as Flexible Frame Composition’ funded by DFG (German Science Foundation). Parts of the research were also funded by the European Research Council (ERC) project TreeGraSP and by the multilingualism part of the European project UNIVERSEH. We would like to thank the anonymous reviewers for their valuable comments.

# Appendix: Attribute-value logic of frames

The appendix describes the attribute-value (AV) logic that underlies the frame approach of this article; see also Kallmeyer and Osswald (2013), who in turn build on Hegner (1994). The logic makes use of two kinds of expressions: *AV formulas* and *AV descriptions*.

AV descriptions are evaluated at frame nodes, formulas on whole frames. AV expressions are defined over a vocabulary $\langle \text{Attr}, \text{Typ}, \text{Rel}, \text{Nnam}, \text{Nvar} \rangle$ consisting of a finite set $\text{Attr}$ of *attribute symbols*, a finite set $\text{Typ}$ of *type symbols*, a finite set $\text{Rel} = \cup_n \text{Rel}_n$ of *relation symbols* (where $\text{Rel}_n$ are *n*-ary relation symbols), a finite set $\text{Nnam}$ of *node names* (or *nominals*), and a countably infinite set $\text{Nvar}$ of *node variables*. The members of $\text{Nlab} = \text{Nnam} \cup \text{Nvar}$ are referred

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /><figcaption>Figure 13: The middle column shows primitive AV descriptions (a) and formulas (b), their notation as AV matrices (right columns), and the structures they denote (left columns)</figcaption></figure>

to as *node labels*. The primitive AV descriptions consist of the following expressions:

$$ (9) \quad t \mid p:t \mid p \doteq q \mid \langle p_1, \dots, p_n \rangle : r \mid p \doteq k $$

with $t \in \text{Typ}$, $r \in \text{Rel}$, $p, q, p_i \in \text{Attr}^*$, and $k \in \text{Nlab}$. The intended meaning of these expressions is depicted in Figure 13a, which also shows the equivalent matrix style notations. The filled circles indicate the nodes at which the expressions are evaluated. Node labels are depicted inside nodes, type symbols are depicted at the outside of nodes.

The set of primitive AV formulas is defined as follows:

$$ (10) \quad k \cdot p:t \mid k \cdot p \doteq l \cdot q \mid \langle k_1 \cdot p_1, \dots, k_n \cdot p_n \rangle : r $$

with $t \in \text{Typ}$, $r \in \text{Rel}$, $p, q, p_i \in \text{Attr}^*$, and $k, l, k_i \in \text{Nlab}$. AV formulas state that there are certain labeled nodes that have certain properties. The intended meaning of AV formulas is sketched in Figure 13b.

Formally, the satisfaction of AV expressions is defined relative to a structure $\langle V, \mathcal{I}, g \rangle$ over the given vocabulary consisting of a set $V$, the *universe* of “nodes”, an *interpretation function* $\mathcal{I}$ defined on $\text{Attr} \cup \text{Typ} \cup \text{Rel} \cup \text{Nnam}$, and a partial *variable assignment function* $g$ from $\text{Nvar}$ to $V$. The func-
tion $\mathcal{I}$ takes members of Attr to partial functions on $V$, members of Typ to subsets of $V$, members of Rel$_n$ to $n$-ary relations on $V$, and members of Nnam to members of $V$. Let $\mathcal{I}_g$ be the partial function from Nlab to $V$ that takes $k$ to $\mathcal{I}(k)$ if $k \in$ Nnam and to $g(k)$ if $k \in$ dom($g$). The members of the image of $\mathcal{I}_g$ are called *labeled nodes*. The interpretation of attributes extends naturally to an interpretation of attribute paths such that $\mathcal{I}(p \cdot f) = \mathcal{I}(f) \circ \mathcal{I}(p)$ for $f \in$ Attr and $p \in$ Attr$^+$. Due to lack of space, we spell out the fairly canonical definitions of satisfaction only for a few cases. For example, primitive descriptions of the form $p : t$ are satisfied at a node $v$ of a structure $(V, \mathcal{I}, g)$, in symbols, $(V, \mathcal{I}, g), v \models p : t$, iff $v \in$ dom($\mathcal{I}(p)$) and $\mathcal{I}(p)(v) \in \mathcal{I}(t)$. By comparison, primitive formulas of the form $k \cdot p \doteq l \cdot q$ are satisfied by a structure $(V, \mathcal{I}, g)$ iff $\{k, l\} \subseteq$ dom($\mathcal{I}_g$), $\mathcal{I}_g(k) \in$ dom($\mathcal{I}(p)$), $\mathcal{I}_g(l) \in$ dom($\mathcal{I}(q)$), and $\mathcal{I}(p)(\mathcal{I}_g(k)) = \mathcal{I}(q)(\mathcal{I}_g(l))$. The AV descriptions and formulas include $\top$ and $\bot$ and are closed under all Boolean operators. The satisfaction relation $\models$ can be extended correspondingly in the usual way.

A *frame* is a structure $(V, \mathcal{I}, g)$ in which every node is accessible from a labeled node by finitely many applications of attribute functions; that is, for every node $v$ there is a node label $k$ and a finite attribute sequence $p$ such that $v = \mathcal{I}(p)(\mathcal{I}_g(k))$.

Given two frames $F = (V, \mathcal{I}, g)$ and $F' = (V', \mathcal{I}', g')$ over $(\text{Attr}, \text{Typ}, \text{Rel}, \text{Nnam}, \text{Nvar})$, $F$ subsumes $F'$, or $F'$ is more informative than $F$, in symbols, $F \subseteq F'$, if there is a function $h$ from $V$ to $V'$ that preserves the labeling and the typing in the frame $F$ as well as its attribute structure and the relations between its nodes. For instance, preservation of the attribute structure of $F$ by $h$ means that $h(v) \in$ dom($\mathcal{I}'(f)$) and $\mathcal{I}'(f)(h(v)) = h(\mathcal{I}(f)(v))$ for $f \in$ Attr and $v \in$ dom($\mathcal{I}(f)$). It is easy to see that if such a function $h$ exists, it is uniquely determined by these conditions. The unification $F \sqcup F'$ of two frames $F$ and $F'$ is their least upper bound with respect to subsumption, if existent.

A frame $F$ is a *model* of an AV formula $\alpha$ iff $F$ satisfies $\alpha$. It is not difficult to see that every finite conjunction of primitive AV formulas has a unique frame model (up to isomorphism) that is *minimal* with respect subsumption. Vice versa, every frame is the minimal model of a finite conjunction of primitive AV formulas.

Frame representations of a certain domain are usually subject to a number of (universal) AV con-

straints that express implicational relations between types and attributes: Types may be (i) subtypes of other types, (ii) imply the presence of certain attributes (and vice versa), etc. Universal constraints have the general form $\forall \varphi$, with $\varphi$ a Boolean AV description. A frame (or structure) satisfies $\forall \varphi$ if each of its nodes satisfies $\varphi$. If $\varphi$ is a Horn description, $\forall \varphi$ is called a *Horn constraint*. Instead of $\forall (\varphi \rightarrow \psi)$, we write $\varphi \Rightarrow \psi$. Given a frame $F$ and a finite set of Horn constraints (which do not generate infinite structures),<fnref n="11" /> there is a unique frame $F'$ subsumed by $F$ that satisfies all the constraints.

## References

* Nicholas Asher. 2011. *Lexical meaning in context: A web of words*. Cambridge University Press.

* William Babonnaud, Laura Kallmeyer, and Rainer Osswald. 2016. Polysemy and coercion – a frame-based approach using LTAG and Hybrid Logic. In *Logical Aspects of Computational Linguistics, 9th International Conference, Lecture Notes in Artificial Intelligence* 10054, pages 18–33, Berlin. Springer.

* Lawrence W. Barsalou. 1992. Frames, concepts, and conceptual fields. In Adrienne Lehrer and Eva Feder Kittay, editors, *Frames, Fields, and Contrasts*, pages 21–74. Lawrence Erlbaum Associates, Hillsdale, NJ.

* Steven Bird, Robert Dale, Bonnie Dorr, Bryan Gibson, Mark Joseph, Min-Yen Kan, Dongwon Lee, Brett Powley, Dragomir Radev, and Yee Fan Tan. 2008. The ACL anthology reference corpus: A reference dataset for bibliographic research in computational linguistics. In *Proceedings of the sixth international conference on language resources and evaluation (LREC’08)*.

* Stergios Chatzikyriakidis and Zhaohui Luo. 2015. In dividuation criteria, dot-types and copredication: A view from modern type theories. In *Proceedings of the 14th Meeting on the Mathematics of Language (MoL 14)*, pages 39–50.

* Robin Cooper. 2011. Copredication, quantification and frames. In Sylvain Pogodalla and Jean-Philippe Prost, editors, *Logical Aspects of Computational Linguistics, 6th International Conference, Lecture Notes in Artificial Intelligence* 6736, pages 64–79. Springer, Berlin.

* Stephen J. Hegner. 1994. Properties of Horn clauses in feature-structure logic. In C. J. Rupp, Michael A. Rosner, and Rod L. Johnson, editors, *Constraints, Language and Computation*, pages 111–147. Academic Press, San Diego, CA.

<footnote n="11">¹¹See Kallmeyer and Osswald (2013, p. 323) for more information about this restriction.</footnote>
tion $\mathcal{I}$ takes members of Attr to partial functions on $V$, members of Typ to subsets of $V$, members of Rel$_n$ to $n$-ary relations on $V$, and members of Nnam to members of $V$. Let $\mathcal{I}_g$ be the partial function from Nlab to $V$ that takes $k$ to $\mathcal{I}(k)$ if $k \in$ Nnam and to $g(k)$ if $k \in$ dom($g$). The members of the image of $\mathcal{I}_g$ are called *labeled nodes*. The interpretation of attributes extends naturally to an interpretation of attribute paths such that $\mathcal{I}(p \cdot f) = \mathcal{I}(f) \circ \mathcal{I}(p)$ for $f \in$ Attr and $p \in$ Attr$^+$. Due to lack of space, we spell out the fairly canonical definitions of satisfaction only for a few cases. For example, primitive descriptions of the form $p : t$ are satisfied at a node $v$ of a structure $(V, \mathcal{I}, g)$, in symbols, $(V, \mathcal{I}, g), v \models p : t$, iff $v \in$ dom($\mathcal{I}(p)$) and $\mathcal{I}(p)(v) \in \mathcal{I}(t)$. By comparison, primitive formulas of the form $k \cdot p \doteq l \cdot q$ are satisfied by a structure $(V, \mathcal{I}, g)$ iff $\{k, l\} \subseteq$ dom($\mathcal{I}_g$), $\mathcal{I}_g(k) \in$ dom($\mathcal{I}(p)$), $\mathcal{I}_g(l) \in$ dom($\mathcal{I}(q)$), and $\mathcal{I}(p)(\mathcal{I}_g(k)) = \mathcal{I}(q)(\mathcal{I}_g(l))$. The AV descriptions and formulas include $\top$ and $\bot$ and are closed under all Boolean operators. The satisfaction relation $\models$ can be extended correspondingly in the usual way.

A *frame* is a structure $(V, \mathcal{I}, g)$ in which every node is accessible from a labeled node by finitely many applications of attribute functions; that is, for every node $v$ there is a node label $k$ and a finite attribute sequence $p$ such that $v = \mathcal{I}(p)(\mathcal{I}_g(k))$.

Given two frames $F = (V, \mathcal{I}, g)$ and $F' = (V', \mathcal{I}', g')$ over $(\text{Attr}, \text{Typ}, \text{Rel}, \text{Nnam}, \text{Nvar})$, $F$ subsumes $F'$, or $F'$ is more informative than $F$, in symbols, $F \subseteq F'$, if there is a function $h$ from $V$ to $V'$ that preserves the labeling and the typing in the frame $F$ as well as its attribute structure and the relations between its nodes. For instance, preservation of the attribute structure of $F$ by $h$ means that $h(v) \in$ dom($\mathcal{I}'(f)$) and $\mathcal{I}'(f)(h(v)) = h(\mathcal{I}(f)(v))$ for $f \in$ Attr and $v \in$ dom($\mathcal{I}(f)$). It is easy to see that if such a function $h$ exists, it is uniquely determined by these conditions. The unification $F \sqcup F'$ of two frames $F$ and $F'$ is their least upper bound with respect to subsumption, if existent.

A frame $F$ is a *model* of an AV formula $\alpha$ iff $F$ satisfies $\alpha$. It is not difficult to see that every finite conjunction of primitive AV formulas has a unique frame model (up to isomorphism) that is *minimal* with respect subsumption. Vice versa, every frame is the minimal model of a finite conjunction of primitive AV formulas.

Frame representations of a certain domain are usually subject to a number of (universal) AV con-

straints that express implicational relations between types and attributes: Types may be (i) subtypes of other types, (ii) imply the presence of certain attributes (and vice versa), etc. Universal constraints have the general form $\forall \varphi$, with $\varphi$ a Boolean AV description. A frame (or structure) satisfies $\forall \varphi$ if each of its nodes satisfies $\varphi$. If $\varphi$ is a Horn description, $\forall \varphi$ is called a *Horn constraint*. Instead of $\forall (\varphi \rightarrow \psi)$, we write $\varphi \Rightarrow \psi$. Given a frame $F$ and a finite set of Horn constraints (which do not generate infinite structures),<fnref n="11" /> there is a unique frame $F'$ subsumed by $F$ that satisfies all the constraints.

## References

Nicholas Asher. 2011. *Lexical meaning in context: A web of words*. Cambridge University Press.

William Babonnaud, Laura Kallmeyer, and Rainer Osswald. 2016. Polysemy and coercion – a frame-based approach using LTAG and Hybrid Logic. In *Logical Aspects of Computational Linguistics, 9th International Conference, Lecture Notes in Artificial Intelligence* 10054, pages 18–33, Berlin. Springer.

Lawrence W. Barsalou. 1992. Frames, concepts, and conceptual fields. In Adrienne Lehrer and Eva Feder Kittay, editors, *Frames, Fields, and Contrasts*, pages 21–74. Lawrence Erlbaum Associates, Hillsdale, NJ.

Steven Bird, Robert Dale, Bonnie Dorr, Bryan Gibson, Mark Joseph, Min-Yen Kan, Dongwon Lee, Brett Powley, Dragomir Radev, and Yee Fan Tan. 2008. The ACL anthology reference corpus: A reference dataset for bibliographic research in computational linguistics. In *Proceedings of the sixth international conference on language resources and evaluation (LREC’08)*.

Stergios Chatzikyriakidis and Zhaohui Luo. 2015. In dividuation criteria, dot-types and copredication: A view from modern type theories. In *Proceedings of the 14th Meeting on the Mathematics of Language (MoL 14)*, pages 39–50.

Robin Cooper. 2011. Copredication, quantification and frames. In Sylvain Pogodalla and Jean-Philippe Prost, editors, *Logical Aspects of Computational Linguistics, 6th International Conference, Lecture Notes in Artificial Intelligence* 6736, pages 64–79. Springer, Berlin.

Stephen J. Hegner. 1994. Properties of Horn clauses in feature-structure logic. In C. J. Rupp, Michael A. Rosner, and Rod L. Johnson, editors, *Constraints, Language and Computation*, pages 111–147. Academic Press, San Diego, CA.

<footnote n="11">¹¹See Kallmeyer and Osswald (2013, p. 323) for more information about this restriction.</footnote>
Elisabetta Jezek and Laure Vieu. 2014. Distributional analysis of copredication: towards distinguishing systematic polysemy from coercion. *Distributional analysis of copredication: towards distinguishing systematic polysemy from coercion*, pages 219–223.

Laura Kallmeyer and Rainer Osswald. 2013. Syntax-driven semantic frame composition in Lexicalized Tree Adjoining Grammars. *Journal of Language Modelling*, 1(2):267–330.

Laura Kallmeyer, Rainer Osswald, and Robert D. Van Valin, Jr. 2013. Tree wrapping for Role and Reference Grammar. In *Formal Grammar (FG 2012/2013)*, number 8036 in Lecture Notes in Computer Science, pages 175–190. Springer.

Eriko Kinoshita, Koji Mineshima, and Daisuke Bekki. 2017. An analysis of selectional restrictions with dependent type semantics. In Setsuya Kurahashi, Yuiko Ohta, Sachiyo Arai, Ken Satoh, and Daisuke Bekki, editors, *New Frontiers in Artificial Intelligence. JAISAI 2016 Workshops. Revised Selected Papers*, number 10247 in Lecture Notes in Computer Science, pages 19–32. Springer, Cham.

Eriko Kinoshita, Koji Mineshima, and Daisuke Bekki. 2018. Coercion as proof search in dependent type semantics. *Oslo Studies in Language*, 10(2):143–162.

Sebastian Löbner. 2014. Evidence for frames from human language. In Thomas Gamerschlag, Doris Gerland, Rainer Osswald, and Wiebke Petersen, editors, *Frames and Concept Types*, number 94 in Studies in Linguistics and Philosophy, pages 23–67. Springer, Dordrecht.

Bruno Mery and Christian Retoré. 2015. Are books events? ontological inclusions as coercive sub-typing, lexical transfers as entailment. In *LENLS: Logic and Engineering of Natural Language Semantics*, pages 74–87.

Elliot Murphy. 2021. *Linguistic representation and processing of copredication*. Ph.D. thesis, UCL (University College London).

James Pustejovsky. 1995. *The Generative Lexicon*. MIT Press, Cambridge, MA.

Matthew J. Traxler, Martin J. Pickering, and Brian McElree. 2002. Coercion in sentence processing: Evidence from eye-movements and self-paced reading. *Journal of Memory and Language*, 47(4):530–547.

Robert D. Van Valin, Jr. 2005. *Exploring the Syntax-Semantics Interface*. Cambridge University Press, Cambridge.
Elisabetta Jezek and Laure Vieu. 2014. Distributional analysis of copredication: towards distinguishing systematic polysemy from coercion. *Distributional analysis of copredication: towards distinguishing systematic polysemy from coercion*, pages 219–223.

Laura Kallmeyer and Rainer Osswald. 2013. Syntax-driven semantic frame composition in Lexicalized Tree Adjoining Grammars. *Journal of Language Modelling*, 1(2):267–330.

Laura Kallmeyer, Rainer Osswald, and Robert D. Van Valin, Jr. 2013. Tree wrapping for Role and Reference Grammar. In *Formal Grammar (FG 2012/2013)*, number 8036 in Lecture Notes in Computer Science, pages 175–190. Springer.

Eriko Kinoshita, Koji Mineshima, and Daisuke Bekki. 2017. An analysis of selectional restrictions with dependent type semantics. In Setsuya Kurahashi, Yuiko Ohta, Sachiyo Arai, Ken Satoh, and Daisuke Bekki, editors, *New Frontiers in Artificial Intelligence. JAISAI 2016 Workshops. Revised Selected Papers*, number 10247 in Lecture Notes in Computer Science, pages 19–32. Springer, Cham.

Eriko Kinoshita, Koji Mineshima, and Daisuke Bekki. 2018. Coercion as proof search in dependent type semantics. *Oslo Studies in Language*, 10(2):143–162.

Sebastian Löbner. 2014. Evidence for frames from human language. In Thomas Gamerschlag, Doris Gerland, Rainer Osswald, and Wiebke Petersen, editors, *Frames and Concept Types*, number 94 in Studies in Linguistics and Philosophy, pages 23–67. Springer, Dordrecht.

Bruno Mery and Christian Retoré. 2015. Are books events? ontological inclusions as coercive sub-typing, lexical transfers as entailment. In *LENLS: Logic and Engineering of Natural Language Semantics*, pages 74–87.

Elliot Murphy. 2021. *Linguistic representation and processing of copredication*. Ph.D. thesis, UCL (University College London).

James Pustejovsky. 1995. *The Generative Lexicon*. MIT Press, Cambridge, MA.

Matthew J. Traxler, Martin J. Pickering, and Brian McElree. 2002. Coercion in sentence processing: Evidence from eye-movements and self-paced reading. *Journal of Memory and Language*, 47(4):530–547.

Robert D. Van Valin, Jr. 2005. *Exploring the Syntax-Semantics Interface*. Cambridge University Press, Cambridge.
VISCOSE – a Kanji Dictionary Enriched with VISual, COmpositional, and SEMantic Information

Werner Winiwarter

University of Vienna

Faculty of Computer Science

Währingerstraße 29

1090 Vienna, Austria

werner.winiwarter

@univie.ac.at

Abstract

In this paper, we present a novel approach for building kanji dictionaries by enriching the lexical data of 3,500 kanji with images, structural decompositions, and semantically based cross-media mappings from the textual to the visual dimension. Our kanji dictionary is part of a Web-based contextual language learning environment based on augmented browsing technology. We display our multimodal kanji information as kanji cards in the Web browser, offering a versatile representation that can be integrated into other advanced creative language learning applications, such as memorization puzzles, creative storytelling assignments, or educational games.

# 1 Introduction

Learning a foreign language can be interesting and exciting. However, when we begin to feel overwhelmed by difficulties, or are faced with uninteresting learning material, it can become tedious and frustrating (Doughty and Long, 2003; VanPatten et al., 2020). Learning Japanese as an English speaker poses a particular challenge (Matsumoto, 2007). It requires extensive memorization due to the drastically different and complicated writing system (Paxton, 2019). The logographic characters called kanji make up its core and are supplemented by two syllabic scripts: *hiragana* and *katakana*. Kanji pose a significant challenge to students who are not aware of this concept (Mori, 2014). There are thousands of these characters and each of them has several possible meanings and pronunciations depending on the usage context (Hermalin, 2015). People born and educated in Japan learn 80-200 kanji per school year, and more advanced characters after high school. Therefore acquiring kanji is a lifelong process. Someone who decides to learn Japanese as a foreign language cannot fall back on this incremental and steady acquisition process, but has to memorize these characters quickly.

Bartholomäus Wloka

University of Vienna

Department for Translation Studies

Gymnasiumstraße 50

1190 Vienna, Austria

bartholomaeus.wloka

@univie.ac.at

# 2 Related Work

In order to build effective methods for the study of kanji, extensive high quality digital resources are needed. The most predominant, freely available kanji dictionary is KANJIDIC2 (Breen, 2004), which contains detailed information about 13,108 characters. *Wiktionary* data is even more comprehensive and accessible as LLOD (Linguistic Linked Open Data) via Dbnary (Sérasset and Tchechmedjiev, 2014; Sérasset, 2015).

Recently, lexical resources have been increasingly enhanced with **visual** representations. Prominent examples are *Wikipedia* thumbnails and other illustrations, also accessible via *DBpedia* (Lehmann et al., 2014). However, the number of images provided varies widely across language versions. *Babelfy* (Moro et al., 2014a,b) is another good example of such efforts, whereas *Wiktionary* contains comparatively few images so far.

On the other hand, *Wiktionary* displays *Ideographic Description Sequences* (IDS) from the *IDS data* set, which is derived from the CHISE project (Morioka, 2008) and is freely available on github<fnref n="1" />. We are aware of only one application that uses this **compositional** data for educational purposes: the *KanjiBuilder* component included in *Kanshudo*<fnref n="2" />. However, the entire content is proprietary, hence it is not available for academic use.

At the **semantic** level, efforts are equally limited. So far, to the best of our knowledge, there exists no approach that maps kanji to *WordNet* (Miller, 1995) *synsets*. However, there are ongoing efforts at the word level in the Japanese WordNet (Isahara et al., 2008). Regarding the mapping of images to WordNet synsets, there is the well-known *ImageNet* collection, which maps about 1,000 images to each synset (Deng et al., 2009). While this is undoubtedly an excellent resource for deep learning

<footnote n="1">¹https://github.com/cjkvci/cjkvci-ids</footnote>

<footnote n="2">²https://www.kanshudo.com/</footnote>
VISCOSE – a Kanji Dictionary Enriched with VISual, COmpositional, and SEMantic Information

Werner Winiwarter

University of Vienna

Faculty of Computer Science

Währingerstraße 29

1090 Vienna, Austria

werner.winiwarter

@univie.ac.at

Bartholomäus Wloka

University of Vienna

Department for Translation Studies

Gymnasiumstraße 50

1190 Vienna, Austria

bartholomaeus.wloka

@univie.ac.at

Abstract

In this paper, we present a novel approach for building kanji dictionaries by enriching the lexical data of 3,500 kanji with images, structural decompositions, and semantically based cross-media mappings from the textual to the visual dimension. Our kanji dictionary is part of a Web-based contextual language learning environment based on augmented browsing technology. We display our multimodal kanji information as kanji cards in the Web browser, offering a versatile representation that can be integrated into other advanced creative language learning applications, such as memorization puzzles, creative storytelling assignments, or educational games.

# 1 Introduction

Learning a foreign language can be interesting and exciting. However, when we begin to feel overwhelmed by difficulties, or are faced with uninteresting learning material, it can become tedious and frustrating (Doughty and Long, 2003; VanPaten et al., 2020). Learning Japanese as an English speaker poses a particular challenge (Matsumoto, 2007). It requires extensive memorization due to the drastically different and complicated writing system (Paxton, 2019). The logographic characters called kanji make up its core and are supplemented by two syllabic scripts: *hiragana* and *katakana*. Kanji pose a significant challenge to students who are not aware of this concept (Mori, 2014). There are thousands of these characters and each of them has several possible meanings and pronunciations depending on the usage context (Hermalin, 2015). People born and educated in Japan learn 80-200 kanji per school year, and more advanced characters after high school. Therefore acquiring kanji is a lifelong process. Someone who decides to learn Japanese as a foreign language cannot fall back on this incremental and steady acquisition process, but has to memorize these characters quickly.

# 2 Related Work

In order to build effective methods for the study of kanji, extensive high quality digital resources are needed. The most predominant, freely available kanji dictionary is KANJIDIC2 (Breen, 2004), which contains detailed information about 13,108 characters. *Wiktionary* data is even more comprehensive and accessible as LLOD (Linguistic Linked Open Data) via Dbnary (Sérasset and Tchechmedjiev, 2014; Sérasset, 2015).

Recently, lexical resources have been increasingly enhanced with **visual** representations. Prominent examples are *Wikipedia* thumbnails and other illustrations, also accessible via *DBpedia* (Lehmann et al., 2014). However, the number of images provided varies widely across language versions. *Babelfy* (Moro et al., 2014a,b) is another good example of such efforts, whereas *Wiktionary* contains comparatively few images so far.

On the other hand, *Wiktionary* displays *Ideographic Description Sequences* (IDS) from the *IDS data* set, which is derived from the CHISE project (Morioka, 2008) and is freely available on github<fnref n="1" />. We are aware of only one application that uses this **compositional** data for educational purposes: the *KanjiBuilder* component included in *Kanshudo*<fnref n="2" />. However, the entire content is proprietary, hence it is not available for academic use.

At the **semantic** level, efforts are equally limited. So far, to the best of our knowledge, there exists no approach that maps kanji to *WordNet* (Miller, 1995) *synsets*. However, there are ongoing efforts at the word level in the Japanese WordNet (Isahara et al., 2008). Regarding the mapping of images to WordNet synsets, there is the well-known *ImageNet* collection, which maps about 1,000 images to each synset (Deng et al., 2009). While this is undoubtedly an excellent resource for deep learning

<footnote n="1">¹https://github.com/cjkvci/cjkvci-ids</footnote>

<footnote n="2">²https://www.kanshudo.com/</footnote>
<figure><img src="image_34.png" /><figcaption>Figure 1: Example of decomposition of a kanji.</figcaption></figure>

ing applications, it is not ideal for educational use, where we often need one well-chosen, representative image. Another effort to assign cliparts from *Openclipart*<fnref n="3" /> to synsets was discontinued after illustrating only 581 synsets (Bond et al., 2009).

The use of flashcards has a long tradition in studying Japanese kanji (Diner and Prasetiani, 2015), e.g. available in *Anki* <fnref n="4" />. We integrate the display of our kanji data as kanji cards into our contextual language learning environment by using augmented browsing technology, as opposed to the traditionally isolated presentation. This way, we can offer advanced *creative learning* (Watts and Blessinger, 2017; Davies et al., 2013) solutions, such as storytelling (Windhaber, 2018) or educational games (Cornillie et al., 2012; Peterson, 2010). As stated very recently in (ELM Learning, 2021), creative learning is vital for several reasons:

* it stimulates **problem solving**, which can be further enhanced by *gamification* techniques;

* it develops **critical thinking** by leaving the

study path up to the learners and enabling to change and shape the material to fit their patterns of thinking;

* it **promotes risk-taking** by leaving the difficulty level up to the student;

* the choice of the learning context encourages a **curious mindset** for the subject matter, fusing the acquisition of new, exciting information with acquisition of language;

* the study of more natural, context-oriented material **increases confidence levels** in everyday usage of the study material.

We included these insightful considerations and combine them with our experience from our long-lasting and ongoing research efforts in refining the structure and presentation of enriched kanji cards (Winiwarter, 2017; Wloka and Winiwarter, 2021b,c). We considered the feedback, critique, and comments we received in the course of this research to develop the novel design of kanji cards presented in this paper.

<footnote n="3">³https://openclipart.org/</footnote>

<footnote n="4">⁴https://apps.ankiweb.net/</footnote>
<figure><img src="image_35.png" /><figcaption>Figure 1: Example of decomposition of a kanji.</figcaption></figure>

ing applications, it is not ideal for educational use, where we often need one well-chosen, representative image. Another effort to assign cliparts from *Openclipart*<fnref n="3" /> to synsets was discontinued after illustrating only 581 synsets (Bond et al., 2009).

The use of flashcards has a long tradition in studying Japanese kanji (Diner and Prasetiani, 2015), e.g. available in *Anki*<fnref n="4" />. We integrate the display of our kanji data as kanji cards into our contextual language learning environment by using augmented browsing technology, as opposed to the traditionally isolated presentation. This way, we can offer advanced *creative learning* (Watts and Blessinger, 2017; Davies et al., 2013) solutions, such as storytelling (Windhaber, 2018) or educational games (Cornillie et al., 2012; Peterson, 2010). As stated very recently in (ELM Learning, 2021), creative learning is vital for several reasons:

* it stimulates **problem solving**, which can be further enhanced by *gamification* techniques;

* it develops **critical thinking** by leaving the

study path up to the learners and enabling to change and shape the material to fit their patterns of thinking;

* it **promotes risk-taking** by leaving the difficulty level up to the student;

* the choice of the learning context encourages **a curious mindset** for the subject matter, fusing the acquisition of new, exciting information with acquisition of language;

* the study of more natural, context-oriented material **increases confidence levels** in everyday usage of the study material.

We included these insightful considerations and combine them with our experience from our long-lasting and ongoing research efforts in refining the structure and presentation of enriched kanji cards (Winiwarter, 2017; Wloka and Winiwarter, 2021b,c). We considered the feedback, critique, and comments we received in the course of this research to develop the novel design of kanji cards presented in this paper.

<footnote n="3">³https://openclipart.org/</footnote>

<footnote n="4">⁴https://apps.ankiweb.net/</footnote>
Our enriched kanji dictionary *VISCOSE* lays the foundation for a novel and innovative method that contributes a vital stimulus for second language acquisition of Japanese. We believe that this will greatly alleviate the difficulty of memorizing kanji for beginners (Kubota, 2017) and keep advanced students motivated.

## 3 Building an Enhanced Kanji Dictionary

We have implemented our kanji dictionary in SWI-Prolog (Wielemaker et al., 2012) because it is an optimal choice for natural language processing tasks as well as the management and retrieval of linguistic data. It is suitable for efficient processing of large XML and RDF files and provides a scalable Web server solution (Wielemaker et al., 2008). We use the latter to generate dynamic Web content for creative language learning scenarios via augmented browsing. We chose to render the kanji information in the Web browser as *kanji cards* because this is an aesthetically pleasing representation, which can be flexibly adapted to many language learning applications, including quizzes and educational games.

The kanji cards in Fig. 1 contain the following information: the radical number, variant forms, on'yomi and kun'yomi readings (described below), glosses, an image, and the ideographic description sequence. The *radical* is the main component of the kanji used for lookup in a paper kanji dictionary. There are 214 radicals in total.

The on'yomi readings are displayed in uppercase, they descend from approximations of original Chinese pronunciations. In contrast, kun'yomi readings are based on pronunciations of native words approximating the meaning of the kanji when it was introduced. Some kanji do not have on'yomi, they are so-called *kokuji*, having been newly created in Japan. We extracted all this lexical data from KANJIDIC2. So far, we performed 460 corrections and additions using mainly Wiktionary as supplementary resource for the 3,500 kanji in our dictionary.

The images displayed on the kanji cards add essential **visual information**. They were all collected manually and exclusively from Wikipedia pages due to licensing reasons and to guarantee access to valuable contextual and ontological data via DBpedia datasets.

We have also added **compositional information** to the kanji cards, represented as an ideographic

description sequence (IDS). We spent extensive editorial work to meticulously check the IDS data resulting in 581 corrections. One reason for this high number was our goal of creating consistent and complete decompositions of all kanji as shown in Fig. 1. This means that we recursively follow the IDS data down to the level of radicals and some additional individual strokes. All the intermediary components have to be valid characters, i.e. they are included in our set of 3,500 kanji. In our visual representation, the kanji cards for radicals are red, same as the radical of the kanji if it occurs in the IDS. Additional radical kanji in the IDS are highlighted in orange.

During the arduous process of gathering suitable images for kanji cards, we soon identified successful strategies for finding visual representations of abstract concepts. We also observed that we often covered several aspects of the lexical data in one image, which sometimes happened even subconsciously. Often these hidden associations were discovered in a review process by a second person. These experiences were the main motivation to start annotating this mapping process to be able to preserve a formalized representation of the image acquisition task. This **semantic information** is a valuable resource for many more advanced language learning applications. However, to reap its full benefit we also had to address the issue of *word sense disambiguation* by assigning each mapping to the correct WordNet synset.

<figure><img src="image_35.png" /><figcaption>Figure 2: Resources used for the information on a kanji card.</figcaption></figure>

A graphical representation of the integrated resources and an example depiction of a kanji card are shown in Fig. 2.

Figure 3 shows an example of three mappings for a kanji card with verbal descriptions and WordNet synset definitions. We have converted this representation into a more formal annotation format as
Our enriched kanji dictionary *VISCOSE* lays the foundation for a novel and innovative method that contributes a vital stimulus for second language acquisition of Japanese. We believe that this will greatly alleviate the difficulty of memorizing kanji for beginners (Kubota, 2017) and keep advanced students motivated.

## 3 Building an Enhanced Kanji Dictionary

We have implemented our kanji dictionary in SWI-Prolog (Wielemaker et al., 2012) because it is an optimal choice for natural language processing tasks as well as the management and retrieval of linguistic data. It is suitable for efficient processing of large XML and RDF files and provides a scalable Web server solution (Wielemaker et al., 2008). We use the latter to generate dynamic Web content for creative language learning scenarios via augmented browsing. We chose to render the kanji information in the Web browser as *kanji cards* because this is an aesthetically pleasing representation, which can be flexibly adapted to many language learning applications, including quizzes and educational games.

The kanji cards in Fig. 1 contain the following information: the radical number, variant forms, on'yomi and kun'yomi readings (described below), glosses, an image, and the ideographic description sequence. The *radical* is the main component of the kanji used for lookup in a paper kanji dictionary. There are 214 radicals in total.

The on'yomi readings are displayed in uppercase, they descend from approximations of original Chinese pronunciations. In contrast, kun'yomi readings are based on pronunciations of native words approximating the meaning of the kanji when it was introduced. Some kanji do not have on'yomi, they are so-called *kokuji*, having been newly created in Japan. We extracted all this lexical data from KANJIDIC2. So far, we performed 460 corrections and additions using mainly Wiktionary as supplementary resource for the 3,500 kanji in our dictionary.

The images displayed on the kanji cards add essential **visual information**. They were all collected manually and exclusively from Wikipedia pages due to licensing reasons and to guarantee access to valuable contextual and ontological data via DBpedia datasets.

We have also added **compositional information** to the kanji cards, represented as an ideographic

description sequence (IDS). We spent extensive editorial work to meticulously check the IDS data resulting in 581 corrections. One reason for this high number was our goal of creating consistent and complete decompositions of all kanji as shown in Fig. 1. This means that we recursively follow the IDS data down to the level of radicals and some additional individual strokes. All the intermediary components have to be valid characters, i.e. they are included in our set of 3,500 kanji. In our visual representation, the kanji cards for radicals are red, same as the radical of the kanji if it occurs in the IDS. Additional radical kanji in the IDS are highlighted in orange.

During the arduous process of gathering suitable images for kanji cards, we soon identified successful strategies for finding visual representations of abstract concepts. We also observed that we often covered several aspects of the lexical data in one image, which sometimes happened even subconsciously. Often these hidden associations were discovered in a review process by a second person. These experiences were the main motivation to start annotating this mapping process to be able to preserve a formalized representation of the image acquisition task. This **semantic information** is a valuable resource for many more advanced language learning applications. However, to reap its full benefit we also had to address the issue of *word sense disambiguation* by assigning each mapping to the correct WordNet synset.

<figure><img src="image_36.png" /><figcaption>Figure 2: Resources used for the information on a kanji card.</figcaption></figure>

A graphical representation of the integrated resources and an example depiction of a kanji card are shown in Fig. 2.

Figure 3 shows an example of three mappings for a kanji card with verbal descriptions and WordNet synset definitions. We have converted this representation into a more formal annotation format as
<figure><img src="image_36.png" /><figcaption>Figure 3: Example of mappings.</figcaption></figure>

a set of septuples:

```
{{(LexicalData, SourceType, InformationType,
    TargetRegion, DepictionType, MappingType, Synset)}
```

For instance, the mappings in Fig. 3 would be
translated into:

```
{{(chop,gloss,activity,main motif,
    real depiction,execution,chop.v.05),
    (tree,radical,tangible object,foreground,
    real depiction,direct,tree.n.01),
    (axe,component,tangible object,detail of main motif,
    real depiction,direct,ax.n.01)}}
```

Of course, we actually use one-letter codes for
the values of the five features to guarantee the effi-

cient storage and processing of the annotation data.
The features are explained in detail in Sect. 4.

We have created annotations for all 3,500 kanji
in our dictionary resulting in a total number of
4,282 annotated mappings. We used altogether 45
different feature values, which are introduced in
the next section and summarized later in Table 1.

# 4 Annotation Examples

In the following subsections, we present detailed
examples of annotations for all values of the five
features that are used to represent the mappings
from the textual kanji information to its visual rep-
resentation.

## 4.1 Source Type

The first example in Fig. 4 shows the mapping of
the *gloss* (*G*) “time”, an *intangible object* (*I*), to
the *real depiction* (*R*) of a sundial, which is the
*main motif* (*M*) of the image. The connection be-
tween the two concepts is established through the
*association* (*A*) that a sundial is used to measure
the time of day. If we look at the *radical* (*R*) 盔
(sun), we can see an association because the sun-
light produces the shadow that indicates the time.
Similarly, we can find an association with the indi-
rect *component* (*C*) 寸 (measurement):

```
{{(time, G, I, M, R, A, clock_time.n.01),
    (sun, R, I, M, R, A, sunlight.n.01),
    (measurement, C, I, M, R, A, measurement.n.01)}}
```

<figure><img src="image_37.png" /><figcaption>Figure 4: Example of source types *G*, *R*, and *C*.</figcaption></figure>

The kanji card in Fig. 5 is an example of mapping
a *partial gloss* (*P*) “tea kettle”, a *tangible object*
(*T*), *directly* (*D*) to its visual representation as *de-
tail of the main motif* (*D*). Partial glosses mainly
concern radicals and a few more exotic cases:

```
{{(kettle lid radical (no. 8), P, T, D, R, D, lid.n.02)}}
```
<figure><img src="image_37.png" /><figcaption>Figure 3: Example of mappings.</figcaption></figure>

a set of septuples:

```
{{(LexicalData, SourceType, InformationType,
    TargetRegion, DepictionType, MappingType, Synset)}
```

For instance, the mappings in Fig. 3 would be
translated into:

```
{{(chop,gloss,activity,main motif,
    real depiction,execution,chop.v.05),
    (tree,radical,tangible object,foreground,
    real depiction,direct,tree.n.01),
    (axe,component,tangible object,detail of main motif,
    real depiction,direct,ax.n.01)}}
```

Of course, we actually use one-letter codes for
the values of the five features to guarantee the effi-

cient storage and processing of the annotation data.
The features are explained in detail in Sect. 4.

We have created annotations for all 3,500 kanji
in our dictionary resulting in a total number of
4,282 annotated mappings. We used altogether 45
different feature values, which are introduced in
the next section and summarized later in Table 1.

# 4 Annotation Examples

In the following subsections, we present detailed
examples of annotations for all values of the five
features that are used to represent the mappings
from the textual kanji information to its visual rep-
resentation.

## 4.1 Source Type

The first example in Fig. 4 shows the mapping of
the *gloss* (*G*) “time”, an *intangible object* (*I*), to
the *real depiction* (*R*) of a sundial, which is the
*main motif* (*M*) of the image. The connection be-
tween the two concepts is established through the
*association* (*A*) that a sundial is used to measure
the time of day. If we look at the *radical* (*R*) 盔
(sun), we can see an association because the sun-
light produces the shadow that indicates the time.
Similarly, we can find an association with the indi-
rect *component* (*C*) 寸 (measurement):

```
{{(time,G,I,M,R,A,clock_time.n.01),
    (sun,R,I,M,R,A,sunlight.n.01),
    (measurement,C,I,M,R,A,measurement.n.01)}}
```

<figure><img src="image_38.png" /><figcaption>Figure 4: Example of source types *G*, *R*, and *C*.</figcaption></figure>

The kanji card in Fig. 5 is an example of mapping
a *partial gloss* (*P*) “tea kettle”, a *tangible object*
(*T*), *directly* (*D*) to its visual representation as *de-
tail of the main motif* (*D*). Partial glosses mainly
concern radicals and a few more exotic cases:

```
{{(kettle lid radical (no. 8), P, T, D, R, D, lid.n.02)}}
```
<figure><img src="image_38.png" /><figcaption>Figure 5: Example of source type *P*.</figcaption></figure>

There are numerous Japanese words that have become English loanwords over the years. In such cases we usually have a mapping from the *kun'yomi* (*K*) reading to the image as in the example in Fig. 6. We can also detect a *component* (*C*) or part meronym relation from the radical 木 (tree) to the fruit as well as an association with the component 市 (market) where the fruit is sold:

({persimmon, G, T, M, R, D, persimmon.n.02),
(kaki, K, T, M, R, D, japanese_persimmon.n.01),
(tree, R, T, M, R, C, tree.n.01),
(market, C, T, M, R, A, marketplace.n.02)}.

<figure><img src="image_39.png" /><figcaption>Figure 6: Example of source type *K*.</figcaption></figure>

Similarly, there exist some mappings of *on'yomi* (*O*) readings, like in the example in Fig. 7. Conveniently, the string “SEN” is also printed on the coin, therefore, we have a *literal* (*L*) mapping:

({.01 yen, G, I, M, R, A, sen.n.01),
(coin, G, T, M, R, D, coin.n.01),
(money, G, I, M, R, A, money.n.03),
(SEN, O, I, D, R, L, sen.n.01)}.

Finally, for some kanji we can match the *glyph* (*G*) of its *logogram* (*L*) with shapes, lines, or curves in the image. Figure 8 illustrates such an example for the kanji 木. The image shows a famous *film scene* (*F*) from the “The Great Dictator” in which the “double cross” symbol can be seen on

<figure><img src="image_40.png" /><figcaption>Figure 7: Example of source type *O*.</figcaption></figure>

Charlie Chaplin's cap. Therefore, in this case, we have a *full match* (*F*):

({(double X radical (no. 89), P, I, D, F, L, x.n.02),
(Ø, L, G, D, F, F, Ø)}.

<figure><img src="image_41.png" /><figcaption>Figure 8: Example of source type *L*.</figcaption></figure>

## 4.2 Information Type

The feature values *I*, *T*, *G* were already introduced in Fig. 4, Fig. 5, and Fig. 8. The two missing information types are *activities* (*A*) and *properties* (*P*). Both can be seen in the annotation for Fig. 9. The image shows a detail from the mural *painting* (*P*) entitled “Labor”. The two men are shown during the *execution* (*E*) of the activity “toil”. From their hard manual work we can infer that they presumably possess the *property* (*P*) “diligent” as well as being “strong” as indicated by the radical 力:

({(toil, G, A, M, P, E, labor.v.02),
(diligent, G, P, M, P, P, diligent.a.02),
(strong, R, P, M, P, P, strong.a.01)}.

## 4.3 Target Region

So far, we have encountered the feature values *M* in Fig. 4 and *D* in Fig. 5. The missing four target regions, which occur less often, are mappings to the *background* (*B*), the *foreground* (*F*), a *secondary motif* (*S*), and the *whole image* (*W*).
<figure><img src="image_39.png" /><figcaption>Figure 5: Example of source type *P*.</figcaption></figure>

There are numerous Japanese words that have become English loanwords over the years. In such cases we usually have a mapping from the *kun'yomi* (*K*) reading to the image as in the example in Fig. 6. We can also detect a *component* (*C*) or part meronym relation from the radical 木 (tree) to the fruit as well as an association with the component 市 (market) where the fruit is sold:

({persimmon, G, T, M, R, D, persimmon.n.02),
(kaki, K, T, M, R, D, japanese_persimmon.n.01),
(tree, R, T, M, R, C, tree.n.01),
(market, C, T, M, R, A, marketplace.n.02)}.

<figure><img src="image_40.png" /><figcaption>Figure 6: Example of source type *K*.</figcaption></figure>

Similarly, there exist some mappings of *on'yomi* (*O*) readings, like in the example in Fig. 7. Conveniently, the string “SEN” is also printed on the coin, therefore, we have a *literal* (*L*) mapping:

({.01 yen, G, I, M, R, A, sen.n.01),
(coin, G, T, M, R, D, coin.n.01),
(money, G, I, M, R, A, money.n.03),
(SEN, O, I, D, R, L, sen.n.01)}.

Finally, for some kanji we can match the *glyph* (*G*) of its *logogram* (*L*) with shapes, lines, or curves in the image. Figure 8 illustrates such an example for the kanji 木. The image shows a famous *film scene* (*F*) from the “The Great Dictator” in which the “double cross” symbol can be seen on

<figure><img src="image_41.png" /><figcaption>Figure 7: Example of source type *O*.</figcaption></figure>

Charlie Chaplin's cap. Therefore, in this case, we have a *full match* (*F*):

({(double X radical (no. 89), P, I, D, F, L, x.n.02),
(Ø, L, G, D, F, F, Ø)}.

<figure><img src="image_42.png" /><figcaption>Figure 8: Example of source type *L*.</figcaption></figure>

## 4.2 Information Type

The feature values *I*, *T*, *G* were already introduced in Fig. 4, Fig. 5, and Fig. 8. The two missing information types are *activities* (*A*) and *properties* (*P*). Both can be seen in the annotation for Fig. 9. The image shows a detail from the mural *painting* (*P*) entitled “Labor”. The two men are shown during the *execution* (*E*) of the activity “toil”. From their hard manual work we can infer that they presumably possess the *property* (*P*) “diligent” as well as being “strong” as indicated by the radical 力:

({(toil, G, A, M, P, E, labor.v.02),
(diligent, G, P, M, P, P, diligent.a.02),
(strong, R, P, M, P, P, strong.a.01)}.

## 4.3 Target Region

So far, we have encountered the feature values *M* in Fig. 4 and *D* in Fig. 5. The missing four target regions, which occur less often, are mappings to the *background* (*B*), the *foreground* (*F*), a *secondary motif* (*S*), and the *whole image* (*W*).