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    <titleInfo>
        <title>Time-Independent and Language-Independent Extraction of Multiword Expressions From Twitter</title>
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    <name type="personal">
        <namePart type="given">Nikhil</namePart>
        <namePart type="family">Londhe</namePart>
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    <name type="personal">
        <namePart type="given">Rohini</namePart>
        <namePart type="family">Srihari</namePart>
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    <name type="personal">
        <namePart type="given">Vishrawas</namePart>
        <namePart type="family">Gopalakrishnan</namePart>
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        <dateIssued>2016-dec</dateIssued>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <publisher>The COLING 2016 Organizing Committee</publisher>
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    <abstract>Multiword Expressions (MWEs) are crucial lexico-semantic units in any language. However, most work on MWEs has been focused on standard monolingual corpora. In this work, we examine MWE usage on Twitter - an inherently multilingual medium with an extremely short average text length that is often replete with grammatical errors. In this work we present a new graph based, language agnostic method for automatically extracting MWEs from tweets. We show how our method outperforms standard Association Measures. We also present a novel unsupervised evaluation technique to ascertain the accuracy of MWE extraction.</abstract>
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        <date>2016-dec</date>
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%0 Conference Proceedings
%T Time-Independent and Language-Independent Extraction of Multiword Expressions From Twitter
%A Londhe, Nikhil
%A Srihari, Rohini
%A Gopalakrishnan, Vishrawas
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 dec
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F londhe-etal-2016-time
%X Multiword Expressions (MWEs) are crucial lexico-semantic units in any language. However, most work on MWEs has been focused on standard monolingual corpora. In this work, we examine MWE usage on Twitter - an inherently multilingual medium with an extremely short average text length that is often replete with grammatical errors. In this work we present a new graph based, language agnostic method for automatically extracting MWEs from tweets. We show how our method outperforms standard Association Measures. We also present a novel unsupervised evaluation technique to ascertain the accuracy of MWE extraction.
%U https://aclanthology.org/C16-1214
%P 2269-2278

Markdown (Informal)

[Time-Independent and Language-Independent Extraction of Multiword Expressions From Twitter](https://aclanthology.org/C16-1214) (Londhe et al., COLING 2016)

• Time-Independent and Language-Independent Extraction of Multiword Expressions From Twitter (Londhe et al., COLING 2016)

ACL

• Nikhil Londhe, Rohini Srihari, and Vishrawas Gopalakrishnan. 2016. Time-Independent and Language-Independent Extraction of Multiword Expressions From Twitter. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2269–2278, Osaka, Japan. The COLING 2016 Organizing Committee.