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        <title>A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection</title>
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            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
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        <name type="personal">
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            <namePart type="family">Martins</namePart>
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        <name type="personal">
            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
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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
%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.
%R 10.18653/v1/2024.findings-acl.451
%U https://aclanthology.org/2024.findings-acl.451/
%U https://doi.org/10.18653/v1/2024.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. Association for Computational Linguistics.