Computer Science ›› 2024, Vol. 51 ›› Issue (6): 338-345.doi: 10.11896/jsjkx.230800198

• Artificial Intelligence • Previous Articles     Next Articles

Gender Discrimination Speech Detection Model Fusing Post Attributes

WANG Xiaolong1,3, WANG Yanhui1,3, ZHANG Shunxiang1,2,3, WANG Caiqin1,3, ZHOU Yuhao1,3   

  1. 1 School of Computer Science and Engineering,Anhui University of Science and Technology,Huainan,Anhui 232001,China
    2 School of Computer,Huainan Normal University,Huainan,Anhui 232038,China
    3 Artificial Intelligence Research Institute of Hefei Comprehensive National Science Center,Hefei 230000,China
  • Received:2023-08-31 Revised:2023-12-04 Online:2024-06-15 Published:2024-06-05
  • About author:WANG Xiaolong,born in 1999.postgraduate,is a member of CCF(No.P8181G).His main research interests include sentiment analysis and data mining.
    ZHANG Shunxiang,born in 1970.Ph.D,professor,Ph.D supervisor.His main research interests include web mining,semantic search,and complex network.
  • Supported by:
    National Natural Science Foundation of China(62076006),Opening Foundation of State Key Laboratory of Cognitive Intelligence,iFLYTEK(COGOS-2023HE02) and University Synergy Innovation Program of Anhui Province(GXXT-2021-08).

Abstract: Gender discrimination speech detection is to identify whether the text has the tendency of gender discrimination through NLP technology,which provides strong support for purifying the network environment.The limitation of current researches is that they pay more attention to the posts itself,while the exploration of relationships among post attributes(user,post,and theme) is overlooked.Motivated by this issue,this paper proposes a model to mine the relationships among post attributes by constructing heterogeneous graphs.Firstly,the word embeddings of post content are generated by ERNIE,subsequently,the contextual dependencies are extracted using BiGRU,and thus the sentence representation is obtained.Then,the heterogeneous graph based on the relationships among post attributes is constructed,and the heterogeneous graph attention network is further employed to obtain the relationship representation of the post.Finally,the sentence representation and relationship representation are fused as input of the Softmax function for classification.Experimental results show that the proposed model can improve the effect of gender discrimination speech detection.

Key words: Gender discrimination speech, Post attributes, BiGRU, Heterogeneous graph, Heterogeneous graph attention network

CLC Number: 

  • TP391
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