Topic Model Combining Topic Word Embedding and Attention Mechanism
摘要
With the popularity of social software,mining effective information from massive digital documents has been a hotspot.The classic topic models including LDA and LSA capture topic information based on word co-occurrence and ignore the context information of words.To address the problem,this paper designs an attention mechanism between words and topics,integrates the topic information and word information into the LDA framework,and on this basis constructs a JEA-LDA topic model.The model uses the attention mechanism between words and topics to merge the word information and topic information into feature representation for topic extraction of the LDA model.The experimental results show that compared with LDA,DMM and other models,the proposed model has better performance in topic coherence and classification tasks,and improves the topic extraction results.