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        <title>A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection</title>
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    <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, C₁ and C₂.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 C₁ and C₂.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.</abstract>
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%0 Conference Proceedings
%T A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection
%A Aida, Taichi
%A Bollegala, Danushka
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F aida-bollegala-2024-semantic
%X 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, C₁ and C₂.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 C₁ and C₂.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.
%U https://aclanthology.org/2024.findings-acl.451
%P 7570-7584

Markdown (Informal)

[A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection](https://aclanthology.org/2024.findings-acl.451) (Aida & Bollegala, Findings 2024)

• A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection (Aida & Bollegala, Findings 2024)

ACL

• Taichi Aida and Danushka Bollegala. 2024. A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection. In Findings of the Association for Computational Linguistics ACL 2024, pages 7570–7584, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.