关键词: 主题模型, 加权关联规则挖掘, 学术论文, 频繁三项集

Abstract: Aiming at the problem that the semantic explanation of the existing topic model is poor and the accuracy is not high, a semi-supervised topic learning and representation method based on association rules and metadata is proposed. Firstly, it uses the metadata of academic papers as a priori knowledge to guide the topic learning, and gets the probability distribution of the term in the document. Then, it gets the frequent three items of each topic by weighted association rule. And then it uses the metadata of academic papers to improve the semantic similarity through the improved vector space model algorithm. Finally, it gets the topic semantics which are more in line with the actual situation and have better semantic explanation. On the same data set of academic papers, three topic learning and representation methods are used to compare experiments. The experimental results show that the method proposed is superior to the others in terms of topic extraction accuracy and topic granularity, and fully validates the effectiveness of the proposed method.

Key words: topic model, weighted association rule mining, academic papers , frequent three-item sets