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        <title>Analyzing Semantic Change through Lexical Replacements</title>
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        <namePart type="given">Francesco</namePart>
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        <namePart type="given">Pierluigi</namePart>
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    <name type="personal">
        <namePart type="given">Haim</namePart>
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    <name type="personal">
        <namePart type="given">Nina</namePart>
        <namePart type="family">Tahmasebi</namePart>
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            <namePart type="given">Andre</namePart>
            <namePart type="family">Martins</namePart>
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            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
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    <abstract>Modern language models are capable of contextualizing words based on their surrounding context. However, this capability is often compromised due to semantic change that leads to words being used in new, unexpected contexts not encountered during pre-training. In this paper, we model semantic change by studying the effect of unexpected contexts introduced by lexical replacements. We propose a replacement schema where a target word is substituted with lexical replacements of varying relatedness, thus simulating different kinds of semantic change. Furthermore, we leverage the replacement schema as a basis for a novel interpretable model for semantic change. We are also the first to evaluate the use of LLaMa for semantic change detection.</abstract>
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    <identifier type="doi">10.18653/v1/2024.acl-long.246</identifier>
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%0 Conference Proceedings
%T Analyzing Semantic Change through Lexical Replacements
%A Periti, Francesco
%A Cassotti, Pierluigi
%A Dubossarsky, Haim
%A Tahmasebi, Nina
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F periti-etal-2024-analyzing
%X Modern language models are capable of contextualizing words based on their surrounding context. However, this capability is often compromised due to semantic change that leads to words being used in new, unexpected contexts not encountered during pre-training. In this paper, we model semantic change by studying the effect of unexpected contexts introduced by lexical replacements. We propose a replacement schema where a target word is substituted with lexical replacements of varying relatedness, thus simulating different kinds of semantic change. Furthermore, we leverage the replacement schema as a basis for a novel interpretable model for semantic change. We are also the first to evaluate the use of LLaMa for semantic change detection.
%R 10.18653/v1/2024.acl-long.246
%U https://aclanthology.org/2024.luhme-long.246/
%U https://doi.org/10.18653/v1/2024.acl-long.246
%P 4495-4510

Markdown (Informal)

[Analyzing Semantic Change through Lexical Replacements](https://aclanthology.org/2024.luhme-long.246/) (Periti et al., ACL 2024)

• Analyzing Semantic Change through Lexical Replacements (Periti et al., ACL 2024)

ACL

• Francesco Periti, Pierluigi Cassotti, Haim Dubossarsky, and Nina Tahmasebi. 2024. Analyzing Semantic Change through Lexical Replacements. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4495–4510, Bangkok, Thailand. Association for Computational Linguistics.