Semantic matching method and system

Abstract

The invention provides a semantic matching method and system. The method includes the steps of conducting vector transformation treatment on any one of a first statement, a second statement, and a third statement in at least one domain, and obtaining a first statement vector, a second statement vector and a third statement vector; inputting the first statement vector and the second statement vector as positive samples, the first statement vector and the third statement vector as negative samples to a recurrent neural network model for training, and obtaining a semantic judgment model used for at least one domain; inputting the sentence vector of a to-be-identified statement of any one target domain in at least one domain and the sentence vectors of candidate statements in the target domain to the semantic judgment model; calculating the similarity degree between the sentence vector of the to-be-identified statement and the sentence vector of each candidate statement in the target domain successively; determining the candidate statement with the highest similarity degree as a target statement which is similar to the semantics of the to-be-identified statement.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a semantic matching method and system. Background technique [0002] Semantic matching tasks often exist in information technology services with QA requirements such as intelligent question answering, intelligent customer service, and question answer retrieval. For example, when using a question answering system based on a question answering library, the system needs to understand the semantics of the questions raised by users and find the same semantic questions in the question answering library, and return the answer. However, due to the flexibility of natural language, users may ask questions in various ways, making the search for matching questions a difficult problem. In the prior art, the solution given is: Existing QASet=(<Q i ,A i >), 1≤i≤n (n is the total number of questions in the Q&A library), where Q i For the questions in the question bank, A i for th...

AI Technical Summary

Problems solved by technology

[0006] The present invention provides a semantic matching method and system to solve the problem of low semantic matching accuracy existing in the semantic matching scheme in the prior art

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