基于注意力机制的事件同指消解方法

doi:10.11896/j.issn.1002-137X.2019.09.029

Abstract

Abstract: Event coreference resolution is a challenging NLP task.It plays an import role in event extraction,QA system and reading comprehension.This paper introduced a decomposable attention neural network model DANGL with global inference mechanism based on remote and local information to document-level event coreference resolution.The neural network model DANGL is quite different from most traditional methods based on probabilistic models and graph models in the past.DANGL first uses Bi-LSTM and CNN to capture both the remote information and the local information of each event mention.Then,it applies the decomposable attention network to capture relatively important information in event mention.Finally,it employs a document-level global inference mechanism to further optimize the coreference chains.Experimental results on TAC-KBP show that DANGL uses a few features and outperforms the state-of-the-art baseline.

Key words: Decomposable attention network, Event coreference, Global inference, Remote and local information

CLC Number:

TP391.1

CHENG Hao-yi, LI Pei-feng, ZHU Qiao-ming. Event Coreference Resolution Method Based on Attention Mechanism[J].Computer Science, 2019, 46(9): 201-205.

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