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        <title>Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation</title>
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    <name type="personal">
        <namePart type="given">Yuya</namePart>
        <namePart type="family">Sawada</namePart>
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    <name type="personal">
        <namePart type="given">Takashi</namePart>
        <namePart type="family">Wada</namePart>
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    <name type="personal">
        <namePart type="given">Takayoshi</namePart>
        <namePart type="family">Shibahara</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Hiroki</namePart>
        <namePart type="family">Teranishi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    </name>
    <name type="personal">
        <namePart type="given">Shuhei</namePart>
        <namePart type="family">Kondo</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    </name>
    <name type="personal">
        <namePart type="given">Hiroyuki</namePart>
        <namePart type="family">Shindo</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Taro</namePart>
        <namePart type="family">Watanabe</namePart>
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    <name type="personal">
        <namePart type="given">Yuji</namePart>
        <namePart type="family">Matsumoto</namePart>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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    <abstract>We propose a simple method for nominal coordination boundary identification. As the main strength of our method, it can identify the coordination boundaries without training on labeled data, and can be applied even if coordination structure annotations are not available. Our system employs pre-trained word embeddings to measure the similarities of words and detects the span of coordination, assuming that conjuncts share syntactic and semantic similarities. We demonstrate that our method yields good results in identifying coordinated noun phrases in the GENIA corpus and is comparable to a recent supervised method for the case when the coordinator conjoins simple noun phrases.</abstract>
    <identifier type="citekey">sawada-etal-2020-coordination</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-main.271</identifier>
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        <date>2020-12</date>
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            <start>3043</start>
            <end>3049</end>
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%0 Conference Proceedings
%T Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation
%A Sawada, Yuya
%A Wada, Takashi
%A Shibahara, Takayoshi
%A Teranishi, Hiroki
%A Kondo, Shuhei
%A Shindo, Hiroyuki
%A Watanabe, Taro
%A Matsumoto, Yuji
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F sawada-etal-2020-coordination
%X We propose a simple method for nominal coordination boundary identification. As the main strength of our method, it can identify the coordination boundaries without training on labeled data, and can be applied even if coordination structure annotations are not available. Our system employs pre-trained word embeddings to measure the similarities of words and detects the span of coordination, assuming that conjuncts share syntactic and semantic similarities. We demonstrate that our method yields good results in identifying coordinated noun phrases in the GENIA corpus and is comparable to a recent supervised method for the case when the coordinator conjoins simple noun phrases.
%R 10.18653/v1/2020.coling-main.271
%U https://aclanthology.org/2020.coling-main.271
%U https://doi.org/10.18653/v1/2020.coling-main.271
%P 3043-3049

Markdown (Informal)

[Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation](https://aclanthology.org/2020.coling-main.271) (Sawada et al., COLING 2020)

• Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation (Sawada et al., COLING 2020)

ACL

• Yuya Sawada, Takashi Wada, Takayoshi Shibahara, Hiroki Teranishi, Shuhei Kondo, Hiroyuki Shindo, Taro Watanabe, and Yuji Matsumoto. 2020. Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3043–3049, Barcelona, Spain (Online). International Committee on Computational Linguistics.