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        <title>Colex2Lang: Language Embeddings from Semantic Typology</title>
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        <namePart type="given">Yiyi</namePart>
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    <abstract>In semantic typology, colexification refers to words with multiple meanings, either related (polysemy) or unrelated (homophony). Studies of cross-linguistic colexification have yielded insights into, e.g., psychology, historical linguistics and cognitive science (Xu et al., 2020; Brochhagen and Boleda, 2022; Schapper and Koptjevskaja-Tamm, 2022). While NLP research up until now has mainly focused on integrating syntactic typology (Naseem et al., 2012; Ponti et al., 2019; Chaudhary et al., 2019; Üstün et al., 2020; Ansell et al., 2021; Oncevay et al., 2022), we here investigate the potential of incorporating semantic typology, of which colexification is an example. We propose a framework for constructing a large-scale synset graph and learning language representations with node embedding algorithms. We demonstrate that cross-lingual colexification patterns provide a distinct signal for modelling language similarity and predicting typological features. Our representations achieve a 9.97% performance gain in predicting lexico-semantic typological features and expectantly contain a weaker syntactic signal. This study is the first attempt to learn language representations and model language similarities using semantic typology at a large scale, setting a new direction for multilingual NLP, especially for low-resource languages.</abstract>
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%0 Conference Proceedings
%T Colex2Lang: Language Embeddings from Semantic Typology
%A Chen, Yiyi
%A Biswas, Russa
%A Bjerva, Johannes
%Y Alumäe, Tanel
%Y Fishel, Mark
%S Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2023
%8 May
%I University of Tartu Library
%C Tórshavn, Faroe Islands
%F chen-etal-2023-colex2lang
%X In semantic typology, colexification refers to words with multiple meanings, either related (polysemy) or unrelated (homophony). Studies of cross-linguistic colexification have yielded insights into, e.g., psychology, historical linguistics and cognitive science (Xu et al., 2020; Brochhagen and Boleda, 2022; Schapper and Koptjevskaja-Tamm, 2022). While NLP research up until now has mainly focused on integrating syntactic typology (Naseem et al., 2012; Ponti et al., 2019; Chaudhary et al., 2019; Üstün et al., 2020; Ansell et al., 2021; Oncevay et al., 2022), we here investigate the potential of incorporating semantic typology, of which colexification is an example. We propose a framework for constructing a large-scale synset graph and learning language representations with node embedding algorithms. We demonstrate that cross-lingual colexification patterns provide a distinct signal for modelling language similarity and predicting typological features. Our representations achieve a 9.97% performance gain in predicting lexico-semantic typological features and expectantly contain a weaker syntactic signal. This study is the first attempt to learn language representations and model language similarities using semantic typology at a large scale, setting a new direction for multilingual NLP, especially for low-resource languages.
%U https://aclanthology.org/2023.nodalida-1.67
%P 673-684

Markdown (Informal)

[Colex2Lang: Language Embeddings from Semantic Typology](https://aclanthology.org/2023.nodalida-1.67) (Chen et al., NoDaLiDa 2023)

• Colex2Lang: Language Embeddings from Semantic Typology (Chen et al., NoDaLiDa 2023)

ACL

• Yiyi Chen, Russa Biswas, and Johannes Bjerva. 2023. Colex2Lang: Language Embeddings from Semantic Typology. In Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pages 673–684, Tórshavn, Faroe Islands. University of Tartu Library.