A two-stage approach for extending event detection to new types via neural networks

Thien Huu Nguyen, Lisheng Fu, Kyunghyun Cho, Ralph Grishman

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

Abstract

We study the event detection problem in the new type extension setting. In particular, our task involves identifying the event instances of a target type that is only specified by a small set of seed instances in text. We want to exploit the large amount of training data available for the other event types to improve the performance of this task. We compare the convolutional neural network model and the feature-based method in this type extension setting to investigate their effectiveness. In addition, we propose a two-stage training algorithm for neural networks that effectively transfers knowledge from the other event types to the target type. The experimental results show that the proposed algorithm outperforms strong baselines for this task.

Original languageEnglish (US)
Title of host publicationProceedings of the 1st Workshop on Representation Learning for NLP, Rep4NLP 2016 at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
EditorsPhil Blunsom, Kyunghyun Cho, Shay Cohen, Edward Grefenstette, Karl Moritz Hermann, Laura Rimell, Jason Weston, Scott Wen-Tau Yih
PublisherAssociation for Computational Linguistics (ACL)
Pages158-165
Number of pages8
ISBN (Electronic)9781945626043
StatePublished - 2016
Event1st Workshop on Representation Learning for NLP, Rep4NLP 2016 at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Berlin, Germany
Duration: Aug 11 2016 → …

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference1st Workshop on Representation Learning for NLP, Rep4NLP 2016 at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
Country/TerritoryGermany
CityBerlin
Period8/11/16 → …

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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