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        <title>Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut</title>
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    <name type="personal">
        <namePart type="given">Nathan</namePart>
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        <namePart type="given">Emily</namePart>
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    <abstract>We present a novel representation, evaluation measure, and supervised models for the task of identifying the multiword expressions (MWEs) in a sentence, resulting in a lexical semantic segmentation. Our approach generalizes a standard chunking representation to encode MWEs containing gaps, thereby enabling efficient sequence tagging algorithms for feature-rich discriminative models. Experiments on a new dataset of English web text offer the first linguistically-driven evaluation of MWE identification with truly heterogeneous expression types. Our statistical sequence model greatly outperforms a lookup-based segmentation procedure, achieving nearly 60% F1 for MWE identification.</abstract>
    <identifier type="citekey">schneider-etal-2014-discriminative</identifier>
    <identifier type="doi">10.1162/tacl_a_00176</identifier>
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        <date>2014</date>
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%0 Journal Article
%T Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut
%A Schneider, Nathan
%A Danchik, Emily
%A Dyer, Chris
%A Smith, Noah A.
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F schneider-etal-2014-discriminative
%X We present a novel representation, evaluation measure, and supervised models for the task of identifying the multiword expressions (MWEs) in a sentence, resulting in a lexical semantic segmentation. Our approach generalizes a standard chunking representation to encode MWEs containing gaps, thereby enabling efficient sequence tagging algorithms for feature-rich discriminative models. Experiments on a new dataset of English web text offer the first linguistically-driven evaluation of MWE identification with truly heterogeneous expression types. Our statistical sequence model greatly outperforms a lookup-based segmentation procedure, achieving nearly 60% F1 for MWE identification.
%R 10.1162/tacl_a_00176
%U https://aclanthology.org/Q14-1016
%U https://doi.org/10.1162/tacl_a_00176
%P 193-206

Markdown (Informal)

[Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut](https://aclanthology.org/Q14-1016) (Schneider et al., TACL 2014)

• Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut (Schneider et al., TACL 2014)

ACL

• Nathan Schneider, Emily Danchik, Chris Dyer, and Noah A. Smith. 2014. Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut. Transactions of the Association for Computational Linguistics, 2:193–206.