Leveraging eventive information for better metaphor detection and classification

I. Hsuan Chen, Yunfei Long, Qin Lu, Chu Ren Huang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Metaphor detection has been both challenging and rewarding in natural language processing applications. This study offers a new approach based on eventive information in detecting metaphors by leveraging the Chinese writing system, which is a culturally bound ontological system organized according to the basic concepts represented by radicals. As such, the information represented is available in all Chinese text without pre-processing. Since metaphor detection is another culturally based conceptual representation, we hypothesize that sub-textual information can facilitate the identification and classification of the types of metaphoric events denoted in Chinese text. We propose a set of syntactic conditions crucial to event structures to improve the model based on the classification of radical groups. With the proposed syntactic conditions, the model achieves a performance of 0.8859 in terms of F-scores, making 1.7% of improvement than the same classifier with only Bag-of-word features. Results show that eventive information can improve the effectiveness of metaphor detection. Event information is rooted in every language, and thus this approach has a high potential to be applied to metaphor detection in other languages.

Original languageEnglish
Title of host publicationCoNLL 2017 - 21st Conference on Computational Natural Language Learning, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages36-46
Number of pages11
ISBN (Electronic)9781945626548
Publication statusPublished - 1 Jan 2017
Event21st Conference on Computational Natural Language Learning, CoNLL 2017 - Vancouver, Canada
Duration: 3 Aug 20174 Aug 2017

Publication series

NameCoNLL 2017 - 21st Conference on Computational Natural Language Learning, Proceedings

Conference

Conference21st Conference on Computational Natural Language Learning, CoNLL 2017
Country/TerritoryCanada
CityVancouver
Period3/08/174/08/17

Keywords

  • metaphor detection
  • eventive information
  • machine learning

ASJC Scopus subject areas

  • Linguistics and Language
  • Artificial Intelligence
  • Human-Computer Interaction

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