Improving event detection with dependency regularization

Kai Cao, Xiang Li, Ralph Grishman

Research output: Contribution to journalConference articlepeer-review


Event Detection (ED) is an Information Extraction task which involves identifying instances of specified types of events in text. Most recent research on Event Detection relies on pattern-based or featurebased approaches, trained on annotated corpora, to recognize combinations of event triggers, arguments, and other contextual information. These combinations may each appear in a variety of linguistic forms. Not all of these event expressions will have appeared in the training data, thus adversely affecting ED performance. In this paper, we demonstrate the effectiveness of Dependency Regularization techniques to generalize the patterns extracted from the training data to boost ED performance. The experimental results on the ACE 2005 corpus show that our pattern-based system with the expanded patterns can achieve 70.49% (with 2.57% absolute improvement) F-measure over the baseline, which advances the state-of-the-art for such systems.

Original languageEnglish (US)
Pages (from-to)78-83
Number of pages6
JournalInternational Conference Recent Advances in Natural Language Processing, RANLP
StatePublished - 2015
Event10th International Conference on Recent Advances in Natural Language Processing, RANLP 2015 - Hissar, Bulgaria
Duration: Sep 7 2015Sep 9 2015

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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