Filtered ranking for bootstrapping in event extraction

Shasha Liao, Ralph Grishman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Several researchers have proposed semi-supervised learning methods for adapting event extraction systems to new event types. This paper investigates two kinds of bootstrapping methods used for event extraction: the document-centric and similarity-centric approaches, and proposes a filtered ranking method that combines the advantages of the two. We use a range of extraction tasks to compare the generality of this method to previous work. We analyze the results using two evaluation metrics and observe the effect of different training corpora. Experiments show that our new ranking method not only achieves higher performance on different evaluation metrics, but also is more stable across different bootstrapping corpora.

Original languageEnglish (US)
Title of host publicationColing 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference
Pages680-688
Number of pages9
Volume2
StatePublished - 2010
Event23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China
Duration: Aug 23 2010Aug 27 2010

Other

Other23rd International Conference on Computational Linguistics, Coling 2010
CountryChina
CityBeijing
Period8/23/108/27/10

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Linguistics and Language

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