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    <titleInfo>
        <title>Building Context-aware Clause Representations for Situation Entity Type Classification</title>
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        <namePart type="given">Zeyu</namePart>
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    <name type="personal">
        <namePart type="given">Ruihong</namePart>
        <namePart type="family">Huang</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Capabilities to categorize a clause based on the type of situation entity (e.g., events, states and generic statements) the clause introduces to the discourse can benefit many NLP applications. Observing that the situation entity type of a clause depends on discourse functions the clause plays in a paragraph and the interpretation of discourse functions depends heavily on paragraph-wide contexts, we propose to build context-aware clause representations for predicting situation entity types of clauses. Specifically, we propose a hierarchical recurrent neural network model to read a whole paragraph at a time and jointly learn representations for all the clauses in the paragraph by extensively modeling context influences and inter-dependencies of clauses. Experimental results show that our model achieves the state-of-the-art performance for clause-level situation entity classification on the genre-rich MASC+Wiki corpus, which approaches human-level performance.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1368</identifier>
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    <part>
        <date>2018-oct-nov</date>
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            <start>3305</start>
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%0 Conference Proceedings
%T Building Context-aware Clause Representations for Situation Entity Type Classification
%A Dai, Zeyu
%A Huang, Ruihong
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F dai-huang-2018-building
%X Capabilities to categorize a clause based on the type of situation entity (e.g., events, states and generic statements) the clause introduces to the discourse can benefit many NLP applications. Observing that the situation entity type of a clause depends on discourse functions the clause plays in a paragraph and the interpretation of discourse functions depends heavily on paragraph-wide contexts, we propose to build context-aware clause representations for predicting situation entity types of clauses. Specifically, we propose a hierarchical recurrent neural network model to read a whole paragraph at a time and jointly learn representations for all the clauses in the paragraph by extensively modeling context influences and inter-dependencies of clauses. Experimental results show that our model achieves the state-of-the-art performance for clause-level situation entity classification on the genre-rich MASC+Wiki corpus, which approaches human-level performance.
%R 10.18653/v1/D18-1368
%U https://aclanthology.org/D18-1368
%U https://doi.org/10.18653/v1/D18-1368
%P 3305-3315

Markdown (Informal)

[Building Context-aware Clause Representations for Situation Entity Type Classification](https://aclanthology.org/D18-1368) (Dai & Huang, EMNLP 2018)

• Building Context-aware Clause Representations for Situation Entity Type Classification (Dai & Huang, EMNLP 2018)

ACL

• Zeyu Dai and Ruihong Huang. 2018. Building Context-aware Clause Representations for Situation Entity Type Classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3305–3315, Brussels, Belgium. Association for Computational Linguistics.