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        <title>Evaluation of automatic collocation extraction methods for language learning</title>
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    <name type="personal">
        <namePart type="given">Vishal</namePart>
        <namePart type="family">Bhalla</namePart>
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        <namePart type="given">Klara</namePart>
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            <title>Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
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        <name type="personal">
            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Claudia</namePart>
            <namePart type="family">Leacock</namePart>
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        <name type="personal">
            <namePart type="given">Nitin</namePart>
            <namePart type="family">Madnani</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        </name>
        <name type="personal">
            <namePart type="given">Ildikó</namePart>
            <namePart type="family">Pilán</namePart>
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        <name type="personal">
            <namePart type="given">Torsten</namePart>
            <namePart type="family">Zesch</namePart>
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    <abstract>A number of methods have been proposed to automatically extract collocations, i.e., conventionalized lexical combinations, from text corpora. However, the attempts to evaluate and compare them with a specific application in mind lag behind. This paper compares three end-to-end resources for collocation learning, all of which used the same corpus but different methods. Adopting a gold-standard evaluation method, the results show that the method of dependency parsing outperforms regex-over-pos in collocation identification. The lexical association measures (AMs) used for collocation ranking perform about the same overall but differently for individual collocation types. Further analysis has also revealed that there are considerable differences between other commonly used AMs.</abstract>
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        <date>2019-08</date>
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            <start>264</start>
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%0 Conference Proceedings
%T Evaluation of automatic collocation extraction methods for language learning
%A Bhalla, Vishal
%A Klimcikova, Klara
%Y Yannakoudakis, Helen
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Zesch, Torsten
%S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F bhalla-klimcikova-2019-evaluation
%X A number of methods have been proposed to automatically extract collocations, i.e., conventionalized lexical combinations, from text corpora. However, the attempts to evaluate and compare them with a specific application in mind lag behind. This paper compares three end-to-end resources for collocation learning, all of which used the same corpus but different methods. Adopting a gold-standard evaluation method, the results show that the method of dependency parsing outperforms regex-over-pos in collocation identification. The lexical association measures (AMs) used for collocation ranking perform about the same overall but differently for individual collocation types. Further analysis has also revealed that there are considerable differences between other commonly used AMs.
%R 10.18653/v1/W19-4428
%U https://aclanthology.org/W19-4428
%U https://doi.org/10.18653/v1/W19-4428
%P 264-274

Markdown (Informal)

[Evaluation of automatic collocation extraction methods for language learning](https://aclanthology.org/W19-4428) (Bhalla & Klimcikova, BEA 2019)

• Evaluation of automatic collocation extraction methods for language learning (Bhalla & Klimcikova, BEA 2019)

ACL

• Vishal Bhalla and Klara Klimcikova. 2019. Evaluation of automatic collocation extraction methods for language learning. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 264–274, Florence, Italy. Association for Computational Linguistics.