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        <title>Modeling Context-sensitive Selectional Preference with Distributed Representations</title>
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        <namePart type="given">Naoya</namePart>
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    <name type="personal">
        <namePart type="given">Yuichiroh</namePart>
        <namePart type="family">Matsubayashi</namePart>
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    <name type="personal">
        <namePart type="given">Masayuki</namePart>
        <namePart type="family">Ono</namePart>
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    <name type="personal">
        <namePart type="given">Naoaki</namePart>
        <namePart type="family">Okazaki</namePart>
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    <name type="personal">
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>This paper proposes a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP). CSP models the narrative consistency between the predicate and preceding contexts of its arguments, in addition to the conventional SP based on semantic types. Furthermore, we present a novel CSP model that extends the neural SP model (Van de Cruys, 2014) to incorporate contextual information into the distributed representations of arguments. Experimental results demonstrate that the proposed CSP model successfully learns CSP and outperforms the conventional SP model in coreference cluster ranking.</abstract>
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%0 Conference Proceedings
%T Modeling Context-sensitive Selectional Preference with Distributed Representations
%A Inoue, Naoya
%A Matsubayashi, Yuichiroh
%A Ono, Masayuki
%A Okazaki, Naoaki
%A Inui, Kentaro
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F inoue-etal-2016-modeling
%X This paper proposes a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP). CSP models the narrative consistency between the predicate and preceding contexts of its arguments, in addition to the conventional SP based on semantic types. Furthermore, we present a novel CSP model that extends the neural SP model (Van de Cruys, 2014) to incorporate contextual information into the distributed representations of arguments. Experimental results demonstrate that the proposed CSP model successfully learns CSP and outperforms the conventional SP model in coreference cluster ranking.
%U https://aclanthology.org/C16-1266
%P 2829-2838

Markdown (Informal)

[Modeling Context-sensitive Selectional Preference with Distributed Representations](https://aclanthology.org/C16-1266) (Inoue et al., COLING 2016)

• Modeling Context-sensitive Selectional Preference with Distributed Representations (Inoue et al., COLING 2016)

ACL

• Naoya Inoue, Yuichiroh Matsubayashi, Masayuki Ono, Naoaki Okazaki, and Kentaro Inui. 2016. Modeling Context-sensitive Selectional Preference with Distributed Representations. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2829–2838, Osaka, Japan. The COLING 2016 Organizing Committee.