C hinese Metaphorical Relation Extraction (2023)

Abstract: Metaphor identification is usually formulated as a sequence labeling or a syntactically related word-pair classification problem. In this paper, we propose a novel formulation of metaphor identification as a relation extraction problem. We introduce metaphorical relations, which are links between two spans, a target span and a source-related span, which are realized in sentences. Based on spans, we can use more flexible and precise text units beyond single words for capturing the properties of the target and the source. We create a dataset for Chinese metaphorical relation extraction, with more than 4,200 sentences annotated with metaphorical relations, corresponding target/source-related spans, and fine-grained span types. We develop a span-based end-to-end model for metaphorical relation extraction and demonstrate its effectiveness. We expect that metaphorical relation extraction can serve as a bridge for connecting linguistic and conceptual metaphor processing. The dataset is at https://github.com/cnunlp/CMRE.

## introduction metaphor is a pervasive linguistic phenomenon in natural language @xcite . it involves expressing non-literal meanings that are not directly derived from the words of the metaphorical expressions and that pose challenges for computational understanding. metaphor is also a cognitive process @xcite . a metaphor consists of 3 parts: the target, the source, and an analogical mapping from the source to the target @xcite , where the target and the source are concepts or conceptual domains. metaphor processing has been an important research field in natural language processing. * co-first authors. metaphor identification is a core task in metaphor processing, which involves recognizing and analyzing metaphorical expressions in text. metaphorical expressions convey a meaning different from their literal interpretation, such as "knowledge is power" and "my car is drinking gas". previous work on metaphor identification has mainly concentrated on two types of expressions. the first type is syntactic constructions such as noun-is-noun (aisb style), verb-subject, adjectivenoun, and verb-direct object constructions @xcite . the motivation is to capture the constructions that violate the selectional preference @xcite . so metaphor identification is formulated as a syntactically-related word-pair classification problem @xcite @xcite . however, this formulation usually requires a parser to extract word pairs that belong to pre-defined checkable relations @xcite , neglecting other types of expressions and ignoring useful contexts in the whole sentence. the second type is annotating the metaphoricity of individual words in a sentence. the motivation is described by the metaphor identification procedure (mip) @xcite that a word would be annotated as metaphorical if its meaning in a specific context is different from its basic meaning. based on this scheme, metaphor identification can be formulated as a sequence labeling task, which has become popular due to the rapid development of deep contextual representation learning @xcite and pretrained language models (plms) @xcite . however, this formulation cannot explicitly reflect the conceptual structure of metaphors. for example, in the sentence "my car is drinking gas", annotating the verb drinking as metaphorical cannot reveal the source-target mapping. in this paper, we propose a novel way to represent metaphors through metaphorical relations. annotating metaphorical relations encompasses the previous two annotation strategies and overcomes their shortcomings. it has the following advantages. first, metaphorical relations use spans instead of words as the basic unit to more precisely capture the characteristics of the target and the source. second, metaphorical relations build surface connections between the target and the source, overcoming the weakness of annotating metaphorical words only. although the source is often implicit, the connections can be built based on the actions, attributes, or parts of the source. for example, my car and drinking form a metaphorical relation, where my car is the target and drinking is an action of the source. third, metaphorical relations can cover multiple types of metaphors that have been discussed in previous work, including aisb style, verb, adjective metaphors, and simi-les since they share a similar underlying cognitive process. so different types of metaphors can be handled in the same framework rather than processed in separate frameworks @xcite . based on the annotated dataset, we explore spanbased models for metaphor identification. we propose an end-to-end model jointly optimizing the span proposer, fine-grained span type classification, and metaphorical relation extraction. specifically, we propose a pair-wise span type classification module to predict the types of a pair of spans simultaneously since they are closely associated and support metaphorical relation scoring through the span type features. experimental results demonstrate the effectiveness of the proposed method compared with the baseline that is directly adapted from an entity relation extraction model. however, there is still room for further improvement in the performance. our method can be used as a baseline for future research. to the best of our knowledge, this is the first work that explores span-based relations to annotate and identify metaphors. metaphorical relations not only reveal the metaphorical expressions but also build surface connections between the target and the source. because the sources are often implicit in the text for metaphors involving metaphorically used verbs and adjectives, we can further infer com-plete source-target mappings based on metaphorical relation extraction. so metaphorical relation extraction has the potential to connect linguistic metaphor identification and conceptual metaphor identification @xcite . we leave this as the future work. some datasets focus on the metaphoricity of syntactically-related word pairs, together with other attributes such as emotion or novelty @xcite @xcite . @xcite built a dataset for annotating source-target domain mappings for metaphorically used verbs, consisting of 761 sentences. the mappings are constructed based on the source and target domain categories borrowed from the master metaphor list @xcite . these datasets are mainly for english. a few datasets cover spanish, russian, and farsi @xcite . @xcite created a chinese dataset for analyzing the emotionality of metaphors, which is a relatively large public dataset for chinese. our work differs from previous work in the annotation scheme. we explicitly annotate metaphorical relations between spans related to the target and the source. this way is more general and expressive compared to annotating individual words or syntactically related word pairs. metaphor identification methods similar to the construction of the datasets, metaphor identification can be viewed as a word-pair classification problem or a word sequence labeling problem. most of the current models depend on recurrent neural networks @xcite @xcite and pre-trained language models @xcite @xcite @xcite @xcite . ## metaphorical relation dataset in this section, we introduce the created chinese metaphorical relation dataset. we first introduce metaphorical relations, then describe the details of data annotation, and finally conduct basic data analysis. ## conclusion we have presented a novel scheme for annotating metaphors in text based on metaphorical relations, which captures the connections between the source and the target through typed spans. metaphorical relations can reveal the structure and context of metaphors, and facilitate new task formulations for metaphor processing. we create a dataset and develop an end-to-end model for metaphorical relation extraction. we show that our model can achieve moderate performance on metaphorical relation extraction. our work has opened up a fresh perspective for metaphor analysis, revealing that metaphorical relations can assume a crucial role in bridging the processing of linguistic metaphors and conceptual metaphors.