A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection

• URL: http://arxiv.org/abs/2403.00226v3
• Date: Sat, 1 Jun 2024 09:23:22 GMT
• Title: A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection
• Authors: Taichi Aida, Danushka Bollegala,
• Abstract summary: A Lexical Semantic Change Detection (SCD) task involves predicting whether a given target word, $w$, changes its meaning between two different text corpora. We propose a supervised two-staged SCD method that uses existing Word-in-Context (WiC) datasets. Experimental results on multiple benchmark datasets for SCD show that our proposed method achieves strong performance in multiple languages.
• Score: 30.563130208194977
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting temporal semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions. Lexical Semantic Change Detection (SCD) task involves predicting whether a given target word, $w$, changes its meaning between two different text corpora, $C_1$ and $C_2$. For this purpose, we propose a supervised two-staged SCD method that uses existing Word-in-Context (WiC) datasets. In the first stage, for a target word $w$, we learn two sense-aware encoders that represent the meaning of $w$ in a given sentence selected from a corpus. Next, in the second stage, we learn a sense-aware distance metric that compares the semantic representations of a target word across all of its occurrences in $C_1$ and $C_2$. Experimental results on multiple benchmark datasets for SCD show that our proposed method achieves strong performance in multiple languages. Additionally, our method achieves significant improvements on WiC benchmarks compared to a sense-aware encoder with conventional distance functions. Source code is available at https://github.com/LivNLP/svp-sdml .

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