Using Conceptual Norms for Metaphor Detection (2020)

Abstract: This paper reports a linguistically-enriched method of detecting token-level metaphors for the second shared task on Metaphor Detection. We participate in all four phases of competition with both datasets, i.e. Verbs and AllPOS on the VUA and the TOFEL datasets. We use the modality exclusivity and embodiment norms for constructing a conceptual representation of the nodes and the context. Our system obtains an F-score of 0.652 for the VUA Verbs track, which is 5% higher than the strong baselines. The experimental results across models and datasets indicate the salient contribution of using modality exclusivity and modality shift information for predicting metaphoricity.

## introduction metaphors are one kind of figurative language that use conceptual mapping to represent one thing (target domain) as another (source domain). as proposed by @xcite in conceptual metaphor theory (cmt), metaphor is not only a property of the language but also a cognitive mechanism that describes our conceptual system. thus metaphors are devices that transfer the property of one domain to another unrelated or different domain, as in 'sweet voice' (use taste to describe sound). metaphors are prevalent in daily life and play a significant role for people to interpret/understand complex concepts. on the other hand, as a popular linguistic device, metaphors encode versatile ontological information, which usually involve e.g. domain transfer @xcite @xcite , sentiment reverse @xcite or modality shift @xcite etc. therefore, detecting the metaphors in texts is essential for capturing the authentic meaning of the texts, which can benefit many natural language processing applications, such as machine translation, dialogue systems and sentiment analysis @xcite . in this shared task, we aim to detect token-level metaphors from plain texts by focusing on content words (verbs, nouns, adjectives and adverbs) of two corpora: vua foot_0 and tofel foot_1 . to better understand the intrinsic properties of metaphors and to provide an in-depth analysis to this phenomenon, we propose a linguisticallyenriched model to deal with this task with the use of modality exclusivity and embodiment norms (see details in section 3). ## related work many approaches have been proposed for automatic detection of metaphors, using features of lexical information @xcite , semantic classes @xcite , concreteness @xcite , word associations @xcite , constructions and frames @xcite and systems such as traditional machine learning classifiers @xcite , deep neural networks @xcite and sequential models @xcite . despite many advances in the above work, metaphor detection remains a challenging task. the semantic and ontological differences between metaphorical and non-metaphorical expressions are often subtle and their perception may vary from person to person. to tackle such problems, researchers resort to specific domain knowledge @xcite ; lexicons @xcite ; supervised methods @xcite @xcite or using attention-based deep learning models to capture latent patterns @xcite . these methods show different strengths on detecting metaphors, yet each has its respective disadvantages, such as having generalization problems or lack association of their results with the intrinsic properties of metaphors. in addition, the reported performances of metaphor detection so far (around 0.6 f1 in the last shared task) @xcite are still not promising. this calls for further endeavours in all aspects. in this work, we adopt supervised machine learning algorithms based on four categories of features, which include linguistic norms, ngram-word, -lemma and -pos collocations, word embeddings and cosine similarity between the target nodes and its neighboring words, as well as the strong baselines provided by the organizer of the shared task @xcite @xcite . moreover, we use several statistical models and ensemble learning strategies during training and testing so as to test the cross-model consistency of the improvement using the various features. the methods are described in detail in the following sections. ## feature sets this work uses four categories of features (16 subsets in all) to represent the nodes and contextual information at hierarchical levels, which include the lexical and syntactic-to-semantic information, sensory modality scales, embodiment ratings (of verbs only), as well as word vectors of the nodes and cosine similarity of node-neighbor pairs, as detailed below. two linguistic norms are used to construct four linguistically-enriched feature sets in the jsonlines format: foot_2 -me (modality exclusivity): 42 dimension of target nodes representation, containing the mapped sensorimotor values in the modality norms; -dm (dominant modality): 1 × 5 dimension of node-neighbor pairs (five lexical neighboring words) information, representing the dominant modality of the target nodes and the surrounding lexical words; -eb (embodiment): 2 dimension of nodes representation, including embodiment rating and standard deviation; -eb-diff (embodiment differences): 2 × 5 dimension of node-neighbor pairs (five lexical neighboring words) information. the me and dm feature sets are constructed by using the lancaster sensorimotor norms collected by lynott et al. (2019). the data include measures of sensorimotor strength (0-5 scale indicating different degrees of sense modalities/action effectors) for 39,707 english words across six perceptual modalities: touch, hearing, smell, taste, vision and interception, and five action effectors: mouth/throat, hand/arm, foot/leg, head (excluding mouth/throat), torso. foot_3 as sensorimotor information plays a fundamental role in cognition, these norms provide a valuable knowledge representation to the conceptual categories of the target and neighboring words which serve as salient features for inferring metaphors. the eb and eb-diff feature sets are constructed by using the embodiment norms for 687 english verbs which is collected by @xcite . research examining semantic richness effects has shown that multiple dimensions of meaning are activated in the process of word recognition @xcite . this data applies the semantic richness approach @xcite to verb stimuli in order to investigate how verb meanings are represented. the relative embodiment ratings (1-7 scale indicating different degrees of bodily involvement) revealed that bodily experience was judged to be more important to the meanings of some verbs (e.g., dance, breathe) than to others (e.g., evaporate, expect), suggesting that relative embodiment is an important aspect of verb meaning, which can be a useful indicator of meaning mismatch of the figurative usage of verbs. these features constitute a rather comprehensive representation of the mismatch of the nodes and their neighbors in terms of senses, domains, modalities, agentivity and concreteness etc, which are highly indicative of metaphorical uses and are hence hypothesized as more distinctive features than the strong baselines in @xcite . in addition, we replicate the three strong baselines provided by the organizer for comparison purposes: ## classifiers and experimental setup three traditional classifiers are used for predicting the metaphoricity of the tokens, including logistic regression, linear svc and a random forest classifier. the machine learning experiments are run through utilities in the scikit-learn laboratory (skll) (pedregosa et al., 2011). 9 for parameter tuning, we use grid search to find optimal parameters for the learners. finally, we set up the following optimized parameters for the three classifiers: logistic regression (lr): 'class weight':'balanced', 'max iter':5000, 'tol':1 • linear svc (lsvc): 'class weight':'balanced', 'max iter':50000, 'c':10 • random forest classifier (rfc): 'min samples split':8, 'max features':'log2', 'oob score':'true', 'random state':10, 'class weight':'balanced' 5 results and discussions ## conclusion we presented a linguistically enhanced method for word-level metaphor detection using conceptual features of modality and embodiment based on traditional classifiers. as suggested by the results, the modality exclusivity and embodiment norms provide conceptual and bodily information for representing the nodes and the context, which help improve the performance of metaphor detection over the three strong baselines to a great extent. it is noteworthy that our system did not employ any deep learning architectures, showing advantages of simplicity and model efficiency, yet it outperforms many sophisticated neural networks. in the future work, we will use the current feature sets in combination with state-of-the-art deep learning models to further examine the effectiveness of this method for metaphor detection.