Multisource semantic analysis based information retrieval method

Abstract

The invention discloses a multisource semantic analysis based information retrieval method. The method comprises the steps that document acquisition and preprocessing are performed; document modeling is performed by utilizing an LDA model, and a reverse index is established; obtaining and preprocessing of user's initial query are performed; multi-dimensional analysis is performed according to the judgment whether queried lexical items are professional medical vocabularies or not, lexical item weighting and query extension are performed based on WordNet and UMLS Metathesaurus; the similarity between a queried extended word set and documents undergoing dimensionality reduction of LDA is calculated, ranking is performed according to progressively decreasing similarity, and the documents which are not lower than a preset threshold value are extracted and returned to a user. The multisource semantic analysis based information retrieval method integrates the characteristics of the WordNet and the UMLS Metathesaurus, conducts multi-dimensional analysis, weighting and extension on the initial query, can make the user's query intention more accurately understood, utilizes the LDA model to perform document modeling, analyzes the document representation capacity of lexical items at hidden theme level and improves the document retrieval performance for the user.

Description

technical field [0001] The invention belongs to the technical field of information retrieval, and in particular relates to an information retrieval method based on multi-source semantic analysis. Background technique [0002] Information retrieval research is a research field that rises with the development of science and technology and the sharp increase of various forms of information. With the popularity of the Internet, medical researchers and doctors often use search engines to obtain the medical information they need. Therefore, how to accurately grasp the user's retrieval intention and how to accurately extract the information that the user is interested in from the massive data Returning information to users has become a primary topic. In response to this problem, the use of query expansion technology to discover and utilize the content of medical literature has become one of the most popular means to improve retrieval performance. [0003] Query expansion is one ...

AI Technical Summary

Problems solved by technology

However, this method is based on the initial query. If the first search result is not good, it may extract words that are not related to the query topic for expansion, resulting in a decrease in query accuracy.

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