Journal of Guangxi Normal University(Natural Science Edition) ›› 2019, Vol. 37 ›› Issue (1): 42-49.doi: 10.16088/j.issn.1001-6600.2019.01.005

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Stance Detection Method Based on Two-Stage Attention Mechanism

YUE Tianchi1, ZHANG Shaowu1,2*, YANG Liang1, LIN Hongfei1, YU Kai2   

  1. 1.School of Computer Science and Technology, Dalian University of Technology, Dalian Liaoning 116024, China;
    2.School of Computer Science and Engineering, Xinjiang University of Finance and Economics, Urumqi Xinjiang 830012, China
  • Received:2018-09-22 Online:2019-01-20 Published:2019-01-08

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Abstract: Stance detection aims to analysize from the text whether the text author is in favor of, against or neutral to the given target, which plays an important part in public opinion analysis. Target dependent stance detection is a challenging task because of the idiomatic phrase and limited contextual information. It is noted that existing methods can not sufficiently model the whole semantic of target and those methods can not jointly model the target and context. To tackle these problems, a two-stage attention model for stance detection is proposed in this paper. Firstly, apply attention mechanism to model target, then match the context with the target representation to obtain attention signal, and finally form the target blended text representation for text classification. Experimental results show the proposed model improves accuracy and F-score by 0.4% and 1.0% respectively on Xinjiang anti-terrorist corpus, and obtain the state of the performance for 4 targets on the NLPCC-2016 stance detection task dataset.

Key words: stance detection, deep learning, attention mechanism, text representation, text classification

CLC Number: 

  • TP391
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