A Hybrid Architecture for Metonymy Detection in Marathi

Abstract

Metonymy, often considered as a figurative trope, is a frequently occurring linguistic phenomenon in which an entity is replaced by a semantically related entity. Named entities are commonly used to refer to associated concepts. For instance, in the sentence India signed a treaty, the geographical name India stands metonymically for the government rather than the physical location. This study develops a hybrid architecture to classify literal and metonymic usages of named entities in Marathi language using small data. The approach integrates Pustejovsky’s Generative Lexicon framework with linguistic features, including part-of-speech tags, named entity labels, and lemmas. The model is evaluated on 890 sentences and achieved F1 scores of 66.98% and 71.97% for literal and metonymic instances, respectively. The study highlights the effectiveness of the features in capturing metonymic contexts, though precision remains a target for improvement. Ablation results confirm that the Formal and Constitutive Qualia roles are the most critical components for detecting metonymic shifts, while the Telic role introduces modest noise in the present corpus. This experiment shows the scope for developing hybrid models for learning non-literal language using small data, which could be beneficial for less-explored and low-resource languages.

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