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        <title>Theoretical Linguistics Rivals Embeddings in Language Clustering for Multilingual Named Entity Recognition</title>
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    <name type="personal">
        <namePart type="given">Sakura</namePart>
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    <name type="personal">
        <namePart type="given">Daisuke</namePart>
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    <name type="personal">
        <namePart type="given">Hiromune</namePart>
        <namePart type="family">Oda</namePart>
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            <namePart type="given">Vishakh</namePart>
            <namePart type="family">Padmakumar</namePart>
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            <namePart type="given">Yao</namePart>
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    <abstract>While embedding-based methods have been dominant in language clustering for multilingual tasks, clustering based on linguistic features has not yet been explored much, as it remains baselines (Tan et al., 2019; Shaffer, 2021). This study investigates whether and how theoretical linguistics improves language clustering for multilingual named entity recognition (NER). We propose two types of language groupings: one based on morpho-syntactic features in a nominal domain and one based on a head parameter. Our NER experiments show that the proposed methods largely outperform a state-of-the-art embedding-based model, suggesting that theoretical linguistics plays a significant role in multilingual learning tasks.</abstract>
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%0 Conference Proceedings
%T Theoretical Linguistics Rivals Embeddings in Language Clustering for Multilingual Named Entity Recognition
%A Imai, Sakura
%A Kawahara, Daisuke
%A Orita, Naho
%A Oda, Hiromune
%Y Padmakumar, Vishakh
%Y Vallejo, Gisela
%Y Fu, Yao
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F imai-etal-2023-theoretical
%X While embedding-based methods have been dominant in language clustering for multilingual tasks, clustering based on linguistic features has not yet been explored much, as it remains baselines (Tan et al., 2019; Shaffer, 2021). This study investigates whether and how theoretical linguistics improves language clustering for multilingual named entity recognition (NER). We propose two types of language groupings: one based on morpho-syntactic features in a nominal domain and one based on a head parameter. Our NER experiments show that the proposed methods largely outperform a state-of-the-art embedding-based model, suggesting that theoretical linguistics plays a significant role in multilingual learning tasks.
%R 10.18653/v1/2023.acl-srw.24
%U https://aclanthology.org/2023.acl-srw.24
%U https://doi.org/10.18653/v1/2023.acl-srw.24
%P 139-151

Markdown (Informal)

[Theoretical Linguistics Rivals Embeddings in Language Clustering for Multilingual Named Entity Recognition](https://aclanthology.org/2023.acl-srw.24) (Imai et al., ACL 2023)

• Theoretical Linguistics Rivals Embeddings in Language Clustering for Multilingual Named Entity Recognition (Imai et al., ACL 2023)

ACL

• Sakura Imai, Daisuke Kawahara, Naho Orita, and Hiromune Oda. 2023. Theoretical Linguistics Rivals Embeddings in Language Clustering for Multilingual Named Entity Recognition. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 139–151, Toronto, Canada. Association for Computational Linguistics.