Neural Networks for Negation Cue Detection in Chinese

Hangfeng He, Federico Fancellu, Bonnie Webber

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

Abstract

Negation cue detection involves identifying the span inherently expressing negation in a negative sentence. In Chinese, negative cue detection is complicated by morphological proprieties of the language. Previous work has shown that negative cue detection in Chinese can benefit from specific lexical and morphemic features, as well as cross-lingual information. We show here that they are not necessary: A bi-directional LSTM can perform equally well, with minimal feature engineering. In particular, the use of a character-based model allows us to capture characteristics of negation cues in Chinese using word embedding information only. Not only does our model performs on par with previous work, further error analysis clarifies what problems remain to be addressed.
Original languageEnglish
Title of host publicationProceedings of the Workshop Computational Semantics Beyond Events and Roles
Place of PublicationValencia, Spain
PublisherAssociation for Computational Linguistics (ACL)
Pages59-63
Number of pages5
DOIs
Publication statusPublished - 4 Apr 2017
EventProceedings of the Workshop Computational Semantics Beyond Events and Roles - Valencia, Spain
Duration: 4 Apr 20174 Apr 2017
http://www.cse.unt.edu/sembear2017/

Conference

ConferenceProceedings of the Workshop Computational Semantics Beyond Events and Roles
Abbreviated titleSemBEaR 2017
CountrySpain
CityValencia
Period4/04/174/04/17
Internet address

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