Dynamically constructing semantic topic hierarchy through formal concept analysis

Dynamic topic analysis can examine the data from different perspectives and know the distribution of data with different correlation degrees thoroughly. It is a challenge to perform dynamic topic analysis on domain text data due to the smaller semantic differences among subtopics. This paper proposes a method of dynamically constructing topic hierarchy, which uses formal concept analysis (FCA)-based information retrieval (IR) as the technical basis and sememes as the semantic basis to perform hierarchical processing from fine-grained to coarse-grained on Chinese domain text data according to the topics of user’s query. It can meet the user’s need for different scales of the query results, and realize multi-angle inspection of the whole dataset and high-precision retrieval of the query. Taking sememes as formal attributes reduces the size of the concept lattice and expands the application of FCA technology to large-scale text data. The sememe-based word meaning identification (WMI) algorithm and semantic similarity measurement method for long text enable the topic hierarchy to be fine, and the coarse and fine filtering strategy renders the FCA-based method more efficient. Experimental results based on the open dataset show that the method proposed is an efficient and flexible topic-based hierarchical approach.