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    <titleInfo>
        <title>Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource</title>
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    <name type="personal">
        <namePart type="given">Qiang</namePart>
        <namePart type="family">Ning</namePart>
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    <name type="personal">
        <namePart type="given">Haoruo</namePart>
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    <name type="personal">
        <namePart type="given">Dan</namePart>
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    <abstract>Extracting temporal relations (before, after, overlapping, etc.) is a key aspect of understanding events described in natural language. We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow. This paper develops such a resource – a probabilistic knowledge base acquired in the news domain – by extracting temporal relations between events from the New York Times (NYT) articles over a 20-year span (1987–2007). We show that existing temporal extraction systems can be improved via this resource. As a byproduct, we also show that interesting statistics can be retrieved from this resource, which can potentially benefit other time-aware tasks. The proposed system and resource are both publicly available.</abstract>
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    <identifier type="doi">10.18653/v1/N18-1077</identifier>
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        <date>2018-06</date>
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%0 Conference Proceedings
%T Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource
%A Ning, Qiang
%A Wu, Hao
%A Peng, Haoruo
%A Roth, Dan
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F ning-etal-2018-improving
%X Extracting temporal relations (before, after, overlapping, etc.) is a key aspect of understanding events described in natural language. We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow. This paper develops such a resource – a probabilistic knowledge base acquired in the news domain – by extracting temporal relations between events from the New York Times (NYT) articles over a 20-year span (1987–2007). We show that existing temporal extraction systems can be improved via this resource. As a byproduct, we also show that interesting statistics can be retrieved from this resource, which can potentially benefit other time-aware tasks. The proposed system and resource are both publicly available.
%R 10.18653/v1/N18-1077
%U https://aclanthology.org/N18-1077
%U https://doi.org/10.18653/v1/N18-1077
%P 841-851

Markdown (Informal)

[Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource](https://aclanthology.org/N18-1077) (Ning et al., NAACL 2018)

• Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource (Ning et al., NAACL 2018)

ACL

• Qiang Ning, Hao Wu, Haoruo Peng, and Dan Roth. 2018. Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 841–851, New Orleans, Louisiana. Association for Computational Linguistics.