Metaphors as Semantic Anchors: A Label-Constrained Contrastive Learning Approach for Chinese Text Classification

Hanqing Tao, Xuesong Wang, Kai Zhang, Enhong Chen, Jun Wang

Abstract: Due to cultural influences and the long history of language evolution, metaphorical expressions are pervasive in Chinese texts, particularly in ironical comments, poetry, and various literary works. Traditional Chinese text classification methods relying on surface-level features often fail to bridge the gap between literal features and label spaces in such texts. To address this problem, we propose L-MAM, a novel label-constrained contrastive learning approach for Chinese text classification based on large language models. Specifically, we first introduce an ambiguity recognition module (ARM) to quantify phonetic and syntactic ambiguities in Chinese characters, identifying expressions that benefit most from metaphorical associations. By leveraging large language model-driven prompts, metaphorical interpretations are extracted from texts, and classification labels are aligned with semantic definitions for deeper contextual understanding. Then, we design a contextual attention mechanism that dynamically adjusts weights based on character ambiguity, and a metaphorical attention mechanism that aligns metaphorical embeddings with label semantics for refined label-constrained associations. Additionally, we devise a contextual-metaphorical contrastive learning mechanism, which employs a triplet loss to differentiate literal and metaphorical aspects while enhancing interclass separability. Finally, we conduct extensive experiments on three real-world datasets, where the experimental results validate the effectiveness of L-MAM, offering new insights into Chinese text classification involving metaphorical comprehension.

External IDs:dblp:journals/tcss/TaoWZCW25