Topic Evolution Research Based on LDA-BERT Similarity Measure Model
Aiming at the problem that the traditional LDA topic model ignores the semantic correlation when extracting text topics,this paper proposes a similarity measure model based on LDA-BERT.Firstly,by combining TF-IDF and TextRank methods,text feature words are extracted and text topics are mined using LDA model.Secondly,by embedding BERT model and combining LDA topic model,the probability distribution of subject-subject words is constructed to represent the topic vector from the level of word granularity.Finally,cosine similarity algorithm is used to calculate the similarity between subjects.Based on the similarity measure model,the vector similarity index was constructed to analyze the correlation between literature research topics,and the knowledge map of topic evolution was drawn.The empirical research was carried out in the field of smart library.It is found that the results calculated by the LDA-BERT model are more accurate than those of the topic LDA model calculations,and more consistent with the actual situation.
万方数据
维普
