基于触发词语义选择的Twitter事件共指消解研究

doi:10.11896／j.issn.1002-137X.2018.12.020

Abstract

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

CLC Number:

TP391

WEI Ping, CHAO Wen-han, LUO Zhun-chen, LI Zhou-jun. Selective Expression Approach Based on Event Trigger for Event Coreference Resolution on Twitter[J].Computer Science, 2018, 45(12): 130-136.

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

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