Word meaning extracting method based on search interactive information and user search intention

Abstract

The invention discloses a word meaning extracting method based on search interactive information and user search intention. The method comprises the following steps in order: recording historic interactive information of each user; using the query keywords in the first K1 rank of the user query frequency to construct a query keyword vector; using corresponding click results obtained after querying the query keywords in the first K2 ranks of the query frequency of each user to construct a corresponding click vector, and setting items corresponding to the query keywords in the first K2 ranks ofthe query frequency of each user in the query keyword vector as 1, and setting other items as zero; extracting a meaning item number of anyone of high-frequency query keywords; clustering the users; and computing the preference rank of the meaning item corresponding to each query keyword of users in the same class. The method can avoid the cost problem caused by manual marking; meanwhile, the problems that the derivative free method has poor effect and is limited by the field. A personalized search service can be provided for a single user according to the analysis result obtained by the method provided by the invention.

Description

[0001] technical field [0002] The invention belongs to the technical field of information retrieval and word meaning disambiguation, and in particular relates to a word meaning extraction method based on search interaction information and user search intention. Background technique [0003] In recent years, the research and application of information retrieval and word sense disambiguation technology are very common, but the research and application of the combination of information retrieval and word sense disambiguation technology is less. [0004] Since the establishment of Google in 1998, information retrieval has gradually become a mainstream technology. Initially, information retrieval provided manual-edited directory retrieval, and the typical company was Yahoo. However, with the explosion of Internet information, manual editing can no longer meet the needs of users. Since then, automatic processing by machines has increasingly become the mainstream. Nowadays, all ...

AI Technical Summary

Problems solved by technology

Among the various methods of word sense disambiguation mentioned above, the human and material costs of manual labeling are very high, and the current word sense disambiguation does not have a method to divide the number of word senses based on user clicks

Faced with the current situation, many companies are also starting to provide personalized search services, but many are still stuck in theories with weak concepts and operability

[0006] There are various word meaning extraction methods in the prior art, but most of the existing methods are to analyze static texts, or manually mark static texts for processing. The former usually needs to limit the text domain, and the general domain The effect is not good; the cost of manual labeling in the latter is very high

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