Automatic Detection of Metaphorical Expressions in Classical Japanese Using WLSP-Enhanced BERT

Abstract

Metaphor detection is a fundamental task in natural language processing, yet research on historical languages remains limited. While progress has been made in modern Japanese metaphor detection, classical Japanese texts present unique challenges due to their distinct vocabulary, grammar, and metaphorical patterns. This paper addresses this gap by applying a BERT-based metaphor detection method enhanced with semantic classification information from the Word List by Semantic Principles (WLSP) to classical Japanese texts. We evaluate our approach on CHJ-Metaphor, a newly available corpus featuring metaphor annotations for three medieval Japanese works from the Corpus of Historical Japanese (CHJ). Our method achieves an F1-score of 82.18 through 5-fold cross-validation. Notably, qualitative analysis by domain experts reveals that our model successfully identifies genuine metaphors overlooked during manual annotation, demonstrating its potential as a tool for improving annotation quality in large-scale corpus construction. These results confirm the effectiveness of WLSP-enhanced approaches for metaphor detection in classical Japanese and suggest promising directions for applying similar techniques to other historical languages.

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