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Text retrieval based on the feature conversion of vector space

This paper presents a text retrieval method based on vector space feature conversion. Even though knowledge-based semantic fingerprint information and semantic information obtained by the Tag-LDA topic model are two different representations of semantic features of the same text, they are incompatible with semantic information. Here we introduce the vector space as a bridge to transfer knowledge-based semantic fingerprint information space into the Tag-LDA model space and prove the rationality of the conversion process with correlation theory. The compatible semantic fingerprint information into the Tag-LDA model generate a new topic model STag-LDA. The Stag-LDA model has certain disambiguation effect for the semantic information of the tag. So, it can mine text semantic information more accurately to improve the retrieval efficiency.