Computer Science ›› 2018, Vol. 45 ›› Issue (9): 243-247.doi: 10.11896/j.issn.1002-137X.2018.09.040

• Artificial Intelligence • Previous Articles     Next Articles

Approach of Stance Detection in Micro-blog Based on Transfer Learning and Multi-representation

ZHOU Yan-fang, ZHOU Gang, LU Zhong-lei   

  1. State Key Laboratory of Mathematical Engineering and Advanced Computing,Zhengzhou 450001,China
  • Received:2017-08-10 Online:2018-09-20 Published:2018-10-10

Abstract: Stance detection aims to identify users’ opinion towards a particular target.Aiming at the problem that exi-sting methods are often difficult to overcome the lack of labeled data and the error caused by word segmentation of Chinese text,this paper presented a transfer learning method and a hybrid model of character-level and word-level features.Firstly,character-level and the word-level features are inputted to deep neural network and the outputs of both are concatenated to reproduce the missing semantic information caused by word segmentation.Then,a topic classification model(parent model) is trained with a large external micro-blog data to obtain the effective sentence feature representation.Next,some of parent model’s parametersare used to initialize stance detection model and the knowledge transferred from auxiliary data can be used to enhance semantic representation ability of sentences.Finally,the labeled data are used to fine tune the child model andtrain classifiers.Experiment on NLPCC-2016 Task 4 proves that F1 value of proposed method achieves 72.2%,which is better than the best one of participating teams.The results show that this approach can improve the stance detection performance and alleviate the influence caused by word segmentation.

Key words: Deep learning, Micro-blog, Stance detection, Transfer learning

CLC Number: 

  • TP391
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