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    <titleInfo>
        <title>Temporal Event Knowledge Acquisition via Identifying Narratives</title>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal “before/after” event knowledge across sentences in narrative stories. The double temporality states that a narrative story often describes a sequence of events following the chronological order and therefore, the temporal order of events matches with their textual order. We explored narratology principles and built a weakly supervised approach that identifies 287k narrative paragraphs from three large corpora. We then extracted rich temporal event knowledge from these narrative paragraphs. Such event knowledge is shown useful to improve temporal relation classification and outperforms several recent neural network models on the narrative cloze task.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1050</identifier>
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%0 Conference Proceedings
%T Temporal Event Knowledge Acquisition via Identifying Narratives
%A Yao, Wenlin
%A Huang, Ruihong
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F yao-huang-2018-temporal
%X Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal “before/after” event knowledge across sentences in narrative stories. The double temporality states that a narrative story often describes a sequence of events following the chronological order and therefore, the temporal order of events matches with their textual order. We explored narratology principles and built a weakly supervised approach that identifies 287k narrative paragraphs from three large corpora. We then extracted rich temporal event knowledge from these narrative paragraphs. Such event knowledge is shown useful to improve temporal relation classification and outperforms several recent neural network models on the narrative cloze task.
%R 10.18653/v1/P18-1050
%U https://aclanthology.org/P18-1050
%U https://doi.org/10.18653/v1/P18-1050
%P 537-547

Markdown (Informal)

[Temporal Event Knowledge Acquisition via Identifying Narratives](https://aclanthology.org/P18-1050) (Yao & Huang, ACL 2018)

• Temporal Event Knowledge Acquisition via Identifying Narratives (Yao & Huang, ACL 2018)

ACL

• Wenlin Yao and Ruihong Huang. 2018. Temporal Event Knowledge Acquisition via Identifying Narratives. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 537–547, Melbourne, Australia. Association for Computational Linguistics.