Temporal Event Knowledge Acquisition via Identifying Narratives
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2018 Association for Computational Linguistics 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 text corpora. We then extracted rich temporal event knowledge from these narrative paragraphs. Such event knowledge is shown useful to improve temporal relation classification and outperform several recent neural network models on the narrative cloze task.
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Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
