A language-independent neural network for event detection

Xiaocheng Feng, Lifu Huang, Duyu Tang, Bing Qin, Heng Ji, Ting Liu

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

Abstract

Event detection remains a challenge due to the difficulty at encoding the word semantics in various contexts. Previous approaches heavily depend on languagespecific knowledge and pre-existing natural language processing (NLP) tools. However, compared to English, not all languages have such resources and tools available. A more promising approach is to automatically learn effective features from data, without relying on languagespecific resources. In this paper, we develop a hybrid neural network to capture both sequence and chunk information from specific contexts, and use them to train an event detector for multiple languages without any manually encoded features. Experiments show that our approach can achieve robust, efficient and accurate results for multiple languages (English, Chinese and Spanish).

Original languageEnglish (US)
Title of host publication54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Short Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages66-71
Number of pages6
ISBN (Electronic)9781510827592
DOIs
StatePublished - 2016
Externally publishedYes
Event54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Berlin, Germany
Duration: Aug 7 2016Aug 12 2016

Publication series

Name54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Short Papers

Other

Other54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
Country/TerritoryGermany
CityBerlin
Period8/7/168/12/16

ASJC Scopus subject areas

  • Language and Linguistics
  • Artificial Intelligence
  • Linguistics and Language
  • Software

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