Personalized Semantic Retrieval System based on Statistical Language Model

The semantic relationship in thesaurus is introduced into the current network information retrieval tool, which can realize semantic retrieval. Using a statistical language model to express query statements and return results in the form of probability distribution can more effectively complete the construction of user model and realize personalized retrieval. Firstly, this paper proposes a similarity calculation method based on the relationship between words in the thesaurus. On the basis of this method, combined with the idea of query expansion and weighted sorting, this paper proposes a semantic retrieval method of forestry information based on the thesaurus. Secondly, this paper uses a statistical language model to propose personalized retrieval methods based on three different user models: topic model, historical model and mixed model. Finally, a forestry information personalized semantic retrieval system is realized by using semantic retrieval and personalized retrieval method. Experimental results indicate that the proposed personalized semantic retrieval method can effectively improve the retrieval performance.