Event Coreference Resolution with Non-Local Information

Jing Lu and Vincent Ng.
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pp. 653-663, 2020.

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Abstract

Existing event coreference resolvers have largely focused on exploiting the information extracted from the local contexts of the event mentions under consideration. Hypothesizing that non-local information could also be useful for event coreference resolution, we present two extensions to a state-of-the-art joint event coreference model that involve incorporating (1) a supervised topic model for improving trigger detection by providing global context, and (2) a preprocessing module that seeks to improve event coreference by discarding unlikely candidate antecedents of an event mention using discourse contexts computed based on salient entities. The resulting model yields the best results reported to date on the KBP 2017 English and Chinese datasets.

BibTeX entry

@InProceedings{Lu+Ng:20a,
  author = {Jing Lu and Vincent Ng},
  title = {Event Coreference Resolution with Non-Local Information},
  booktitle = {Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
  pages = {653--663}, 
  year = 2020}