Bridging Word-Pair and Token-Level Metaphor Detection with Explainable Domain Mining (2024)

Abstract: Metaphor detection aims to identify whether a linguistic expression in text is metaphorical or literal. Most existing research tackles this problem either using word-pair or token-level information as input, and thus treats word-pair and token-level metaphor detection as distinct subtasks. Benefited from the simplified structure of word pairs, recent methods for word-pair metaphor detection can provide intermediate explainable clues for the detection results, which remains a challenging issue for token-level metaphor detection. To mitigate this issue in token-level metaphor detection and take advantage of word pairs, in this paper, we make the first attempt to bridge word-pair and token-level metaphor detection via modeling word pairs within a sentence as explainable intermediate information. As the central role of verb in metaphorical expressions, we focus on token-level verb metaphor detection and propose a novel explainable Word Pair based Domain Mining (WPDM) method. Our work is inspired by conceptual metaphor theory (CMT). We first devise an approach for conceptual domain mining utilizing semantic role mapping and resources at cognitive, commonsense and lexical levels. We then leverage the inconsistency between source and target domains for core word pair modeling to facilitate the explainability. Experiments on four datasets verify the effectiveness of our method and demonstrate its capability to provide the core word pair and corresponding conceptual domains as explainable clues for metaphor detection.

## introduction metaphor is not just a figurative expression but a pervasive phenomenon in human thought, perception, and reasoning @xcite . as defined in merriam-webster dictionary, metaphor is "a figure of speech in which a word/phrase literally denoting one kind of object or idea is used in ## related work metaphor detection aims to distinguish metaphorical and literal expressions in text. existing studies focus on two subtasks: word-pair metaphor detection and token-level metaphor detection. the former determines whether a word pair is metaphorical or literal, and the latter concentrates on identifying metaphorical words within sentences. ## problem definition formally, d @xmath0, w k v , b k )} ntr @xmath1, where s is a sentence, w v is a focus verb within s, and b is the label (metaphorical or literal) for ## method we propose an explainable word pair based domain mining (wpdm) method for token-level verb metaphor detection. figure 2 illustrates the overview of our method, which contains four components: (1) literal example sentences construction, which collects example sentences for the literal meaning of the focus verb; (2) semantic role mapping, which organizes words labeled with the same semantic role into a semantic group in the input sentence and literal example sentences, respectively; (3) conceptual domain mining, which ## conclusion in this paper, we propose an explainable word pair based domain mining method for tokenlevel metaphor detection. inspired by conceptual metaphor theory, our method leverages semantic role mapping and conceptual source and target domain mining for core word pair modeling to facilitate explainable metaphor detection. experimental results not only verify the effectiveness of our method for metaphor detection but also demonstrate its capability to determine the core word pair and conceptual domain information as explainable clues for the prediction of metaphors.