Full-text retrieval system based on two-level semantic analysis

Abstract

The invention belongs to the technical field of information retrieval, and provides a full-text retrieval system based on two-level semantic analysis. The system comprises a query information receiving module, a sentence summary level semantic processing module, a retrieval module, an index library, an index module, a text level semantic processing module, a result set processing module and a data server. The system performs summary processing and repeat processing for a query statement to repeat the query statement submitted by a user from sentence summary level semanteme, and latent semantic information and thematic information are extracted in the text level to eliminate retrieval result documents not conforming to text level semanteme, so that the full-text retrieval system achieves two-level semantic analysis in the 'sentence summary level' and the 'text level'. By improving semantic processing capacity of query statements and text sets, the full-text retrieval system is more human-friendly and intelligent, and provides more convenient and accurate services for users.

Description

technical field [0001] The invention belongs to information retrieval technology, in particular to a full-text retrieval system based on double-layer semantic analysis. technical background [0002] With the continuous exponential growth of Internet information, people have entered an era of information explosion. How to accurately and efficiently find the data users need in massive amounts of data is a huge challenge. The birth of search engines has solved the above problems to a certain extent. Search engines use specific computer programs to collect information from the Internet according to certain strategies, organize and process the information, provide users with retrieval services, and display the relevant information retrieved by users to the user's system. The vast majority of search engines use information retrieval technology to establish document inverted index databases, query keywords or query statements submitted by users, and return relevant document resul...

AI Technical Summary

Problems solved by technology

However, because it has not yet adopted deep semantic analysis technology, many highly relevant retrieval results are lost

On the other hand, because information retrieval cannot "understand" the semantics of document chapter level, but simply considers a document as a collection of words, this also causes some highly relevant documents to be placed in relatively close After the position, it affects the accuracy of the retrieval

Because of this, the traditional information retrieval system lacks semantic analysis ability for retrieved documents, so it cannot perform semantic retrieval accurately.

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