Query expansion method based on multisource positive and negative external feedback information

Abstract

The invention discloses a query expansion method based on multisource positive and negative external feedback information. An expansion risk is reduced by introducing a regular constraint into a processing of fusing external query information; therefore, new query can be rapidly and effectively built, and thus a search result more conforms to user needs. Compared with a traditional feedback search method, the technical scheme of the method provided by the invention has the effect of significantly enhanced performance.

Description

technical field [0001] The invention belongs to the field of word information processing, in particular to a query expansion method based on multi-source positive and negative external feedback information. Background technique [0002] The emergence of social media (such as Twitter, Facebook, Google+) has profoundly changed the way people produce and consume information. The biggest difference between him and mainstream news media websites (such as CNN or nytimes) is that people in social networks are information Consumers are also producers of information. Since the sources of information in social networks are not only diverse but also chaotic, this increases the difficulty for users to obtain information. [0003] The traditional method query expansion methods are mainly divided into two types according to the different expansion sources: 1) local query expansion methods using local query document sets as the expansion source; 2) global expansion methods using external k...

AI Technical Summary

Problems solved by technology

The first method mostly uses methods such as text clustering, latent semantic indexing (LSI for short) and similarity dictionaries to expand the query. However, due to the relatively fixed composition of the local corpus and the small scale, it cannot well reflect the real user experience. query intent

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