Query representation and hybrid retrieval model construction method based on context sensing theme

Abstract

The invention discloses a query representation and hybrid retrieval model construction method based on a context sensing theme. The method includes the following steps that firstly, a pseudo relevance feedback document of query is obtained on the basis of a keyword set of query, and context related to query is selected from the pseudo relevance feedback document; secondly, a context sensing theme model is introduced, the context is fused into the context sensing theme model, and implicit theme information of a context window is mined on the basis of a corpus theme to obtain a corresponding theme vector; thirdly, query is represented by combining the theme vector and the keyword set, a hybrid retrieval model is constructed on the basis of the theme vector and the keyword set, and a final retrieval score is obtained.

Description

technical field [0001] The invention relates to the technical field of Internet information retrieval, in particular to a method for establishing a query representation and a hybrid retrieval model based on a context-aware topic model. Background technique [0002] Query representation has always been the core of the field of information retrieval, and the most common problem is that the user query is too short (contains only a few keywords), and it is easy to cause the relevant documents in the retrieval process to not match the query. For example, for the user query "short of water", if the document contains words related to the query such as "drought", although the correlation is high, but because the original query keyword "short of water" is not included, the final matching degree will be very low. low, thereby affecting the accuracy of the query. [0003] A common solution is query expansion based on pseudo-relevance feedback. This method is based on the preliminary ...

AI Technical Summary

Problems solved by technology

However, the existing extended word selection methods generally only consider the co-occurrence of the extended word and the original query word in the context window of the pseudo-relevance feedback, and there are still the following problems: (1) It is necessary to explicitly select which words to use as the final query Extension, some irrelevant words, even "harmful words" will still be introduced in the unsupervised situation

For example, in articles involving various environmental resources, the keyword "water shortage" appears frequently, but similar words such as "hydroelectric power" and "natural gas" also appear in its context, which will deviate from the original query and reduce the accuracy of the query. (2) The final query representation is still based on the dictionary space, ignoring the semantic information implicit in the query, such as potential topics; (3) The retrieval model based on this query representation mainly considers keyword matching, but ignores the relationship between documents and queries. Matching at the Semantic Level

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