Gender Discrimination Speech Detection Model Fusing Post Attributes
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 resear-ches 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 con-textual 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 em-ployed 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.
NETL
NSTL
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