Computer Science ›› 2018, Vol. 45 ›› Issue (12): 130-136,147.doi: 10.11896/j.issn.1002-137X.2018.12.020

• Artificial Intelligence • Previous Articles     Next Articles

Selective Expression Approach Based on Event Trigger for Event Coreference Resolution on Twitter

WEI Ping1, CHAO Wen-han1, LUO Zhun-chen2, LI Zhou-jun1   

  1. (School of Computer Science and Engineering,Beihang University,Beijing 100191,China)1
    (Information Research Center of Military Science,PLA Academy of Military Science,Beijing 100142,China)2
  • Received:2018-01-24 Online:2018-12-15 Published:2019-02-25

Abstract: With the development and popularization of social media,how to recognize the coreference relation between two event mention in short texts is an urgent issue.In traditional researches about event coreference resolution,a rich set of linguistic features derived from pre-existing NLP tools and various knowledge bases is required,which restricts domain scalability and leads to the propagation of errors.To overcome these limitations,this paper proposed a novel selective expression approach based on event trigger to explore the coreference relationship on Twitter.Firstly,a bi-direction long short term memory (Bi-LSTM) is exploited to extract the features at sentence level and at mention level.Then,the latent features are generated by applying a gate on sentence level features to make it selectively express.Next,two auxiliary features named the overlapped words of trigger and time interval are designed.Finally,all these features are concatenated and fed into a simple classifier to predict the coreference relationship.In order to evaluate this method,this paper annotated a new dataset EventCoreOnTweet (ECT).The experimental results demonstrate that the selective expression approach significantly improves the performance of coreference resolution of short texts.

Key words: Event coreference resolution, Short text, Bi-direction long short-term memory, Neural networks

CLC Number: 

  • TP391
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