Metaphorical Polysemy Detection: Conventional Metaphor Meets Word Sense Disambiguation (2022)

Abstract: Linguists distinguish between novel and conventional metaphor, a distinction which the metaphor detection task in NLP does not take into account. Instead, metaphoricity is formulated as a property of a token in a sentence, regardless of metaphor type. In this paper, we investigate the limitations of treating conventional metaphors in this way, and advocate for an alternative which we name ‘metaphorical polysemy detection’ (MPD). In MPD, only conventional metaphoricity is treated, and it is formulated as a property of word senses in a lexicon. We develop the first MPD model, which learns to identify conventional metaphors in the English WordNet. To train it, we present a novel training procedure that combines metaphor detection with ‘word sense disambiguation’ (WSD). For evaluation, we manually annotate metaphor in two subsets of WordNet. Our model significantly outperforms a strong baseline based on a state-of-the-art metaphor detection model, attaining an ROC-AUC score of .78 (compared to .65) on one of the sets. Additionally, when paired with a WSD model, our approach outperforms a state-of-the-art metaphor detection model at identifying conventional metaphors in text (.659 F1 compared to .626).

## introduction linguists differentiate between two types of metaphor: novel and conventional. while novel metaphors are creative expressions made in a particular situation by one particular individual, conventional metaphors are those which have been widely adopted by a language community. consider: example (1) is a literal usage of attack which refers to a military offensive. example (2) is a conventional metaphor referring to intense verbal criticism. detecting novel metaphors and conventional metaphors are fundamentally different tasks. novel metaphors are creative usages of words, which violate statistical patterns of language (e.g. @xcite , and which extend the meaning of a word into unexpected areas of semantic space. conventional metaphors, on the other hand, can be considered lexicalised: examples (1) and (2) come from the gloss of two senses of attack in wordnet @xcite , where the novel sense of torpedoed in example (3) is not yet captured. despite these inherent differences, in nlp the standard metaphor detection task, abbreviated as smd here, makes no distinction between novel and conventional metaphor. although recent works have produced resources which distinguish between novel and conventional metaphors @xcite , both metaphor types are annotated as properties of tokens, suitable for smd. in this work, we demonstrate the shortcomings of this formulation, and argue that conventional metaphoricity is best treated not as a property of word occurrences in a sentence, but of word senses in a lexicon. with this ## metaphor in the lexicon a fundamental feature of language is polysemy, the phenomenon of a wordform exhibiting multiple meanings which are systematically related. when meanings are related by metaphorical similarity, the phenomenon is called metaphorical polysemy. an example is the word attack, which describes either a physical confrontation, ormetaphorically-heated criticism (e.g. "she attacked my arguments"). instances of metaphorical polysemy are called conventional metaphors. in this section, we describe how conventional metaphors materialise in lexica ( §2.1). with this in mind, we illustrate the shortcomings of the existing metaphor detection task ( §2.2), and propose an alternative formulation which is more suitable for conventional metaphors ( §2.3). ## learning conventional metaphor in this section, we describe the first mpd model ( §3.1). to train it, it is embedded into an smd pipeline ( §3.2). ## evaluation in this section, we describe the evaluation data we collected for mpd ( §4.1), and introduce relative metaphoricity, an aspect of metaphor which it is only possible to evaluate with mpd ( §4.2). we then describe our experimental setup ( §4.3) and results ( §4.4). ## conclusion and future work in this paper, we argued that the standard metaphor detection (smd) task in nlp is ill-suited to conventional metaphor, because it conflates metaphor detection with word sense disambiguation (wsd). as an alternative, we proposed metaphorical polysemy detection (mpd). we constructed the first mpd model, which identifies conventional metaphors in wordnet. to train it, we employed a novel training technique which exploits resources designed for smd and wsd. to evaluate mpd, we collected two sets of evaluation data, and proposed a new performance measure based on relative judgements of metaphoricity. our model performed significantly better than a state-of-the-art smd model in all mpd evaluation settings (e.g. attaining .78 roc-auc and .60 f1 on a subset based on the master metaphor list). additionally, we found that pairing our model with a wsd model led to state-of-theart results for token-based conventional metaphor detection (.659 compared to .626). we make our code and mpd evaluation data publicly available. 8 making serious improvements in mpd will require the collection of dedicated training data. training mpd using smd data necessitated the inclusion of a wsd model, which will negatively affect mpd performance when it makes mistakes. additionally, our model did not utilise information about the set of definitions a wordform is associated with (d w ), and instead made the naïve assumption that this information will be encoded in a static embedding of w. set-and graph-based architectures which exploited this information would be a natural thing to explore. with more data and better models, it may be possible to synthesise a full set of wordnet metaphoricity annotation, and even to extend this synthetic annotation to multilingual wordnet versions. this would be a valuable resource, which would open the door to study other questions surrounding metaphorical polysemy. omnia zayed, john philip mccrae, and paul buitelaar.