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    <titleInfo>
        <title>Representation and Learning of Temporal Relations</title>
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    <name type="personal">
        <namePart type="given">Leon</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>Determining the relative order of events and times described in text is an important problem in natural language processing. It is also a difficult one: general state-of-the-art performance has been stuck at a relatively low ceiling for years. We investigate the representation of temporal relations, and empirically evaluate the effect that various temporal relation representations have on machine learning performance. While machine learning performance decreases with increased representational expressiveness, not all representation simplifications have equal impact.</abstract>
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    <part>
        <date>2016-12</date>
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            <start>1937</start>
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%0 Conference Proceedings
%T Representation and Learning of Temporal Relations
%A Derczynski, Leon
%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 derczynski-2016-representation
%X Determining the relative order of events and times described in text is an important problem in natural language processing. It is also a difficult one: general state-of-the-art performance has been stuck at a relatively low ceiling for years. We investigate the representation of temporal relations, and empirically evaluate the effect that various temporal relation representations have on machine learning performance. While machine learning performance decreases with increased representational expressiveness, not all representation simplifications have equal impact.
%U https://aclanthology.org/C16-1182
%P 1937-1948

Markdown (Informal)

[Representation and Learning of Temporal Relations](https://aclanthology.org/C16-1182) (Derczynski, COLING 2016)

• Representation and Learning of Temporal Relations (Derczynski, COLING 2016)

ACL

• Leon Derczynski. 2016. Representation and Learning of Temporal Relations. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1937–1948, Osaka, Japan. The COLING 2016 Organizing Committee.