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        <title>Inducing a lexicon of sociolinguistic variables from code-mixed text</title>
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        <namePart type="given">Philippa</namePart>
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            <title>Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text</title>
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    <abstract>Sociolinguistics is often concerned with how variants of a linguistic item (e.g., nothing vs. nothin’) are used by different groups or in different situations. We introduce the task of inducing lexical variables from code-mixed text: that is, identifying equivalence pairs such as (football, fitba) along with their linguistic code (football→British, fitba→Scottish). We adapt a framework for identifying gender-biased word pairs to this new task, and present results on three different pairs of English dialects, using tweets as the code-mixed text. Our system achieves precision of over 70% for two of these three datasets, and produces useful results even without extensive parameter tuning. Our success in adapting this framework from gender to language variety suggests that it could be used to discover other types of analogous pairs as well.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6101</identifier>
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%0 Conference Proceedings
%T Inducing a lexicon of sociolinguistic variables from code-mixed text
%A Shoemark, Philippa
%A Kirby, James
%A Goldwater, Sharon
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F shoemark-etal-2018-inducing
%X Sociolinguistics is often concerned with how variants of a linguistic item (e.g., nothing vs. nothin’) are used by different groups or in different situations. We introduce the task of inducing lexical variables from code-mixed text: that is, identifying equivalence pairs such as (football, fitba) along with their linguistic code (football→British, fitba→Scottish). We adapt a framework for identifying gender-biased word pairs to this new task, and present results on three different pairs of English dialects, using tweets as the code-mixed text. Our system achieves precision of over 70% for two of these three datasets, and produces useful results even without extensive parameter tuning. Our success in adapting this framework from gender to language variety suggests that it could be used to discover other types of analogous pairs as well.
%R 10.18653/v1/W18-6101
%U https://aclanthology.org/W18-6101
%U https://doi.org/10.18653/v1/W18-6101
%P 1-6

Markdown (Informal)

[Inducing a lexicon of sociolinguistic variables from code-mixed text](https://aclanthology.org/W18-6101) (Shoemark et al., WNUT 2018)

• Inducing a lexicon of sociolinguistic variables from code-mixed text (Shoemark et al., WNUT 2018)

ACL

• Philippa Shoemark, James Kirby, and Sharon Goldwater. 2018. Inducing a lexicon of sociolinguistic variables from code-mixed text. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 1–6, Brussels, Belgium. Association for Computational Linguistics.