Semantic matching method and device for question and answer text, medium and electronic equipment

Abstract

The invention provides a semantic matching method for question and answer texts. The method can effectively solve problems in related technologies. For example, in the question and answer text semantic matching technology based on a deep learning model in the related technology, only context local semantic feature information can be provided, background global feature information and syntactic feature information of a question and answer text are lacked, feature unification is caused, and semantic matching information of the question and answer text cannot be completely embodied. The inventionprovides a question and answer text semantic matching method based on multi-level features and deep learning, word and syntactic structure distributed representation is carried out on words and syntactic information of the question and answer text; and the context local feature information and the syntactic structure feature information of the question and answer text are extracted by using the recurrent neural network, and then the background global feature information is extracted by using an attention mechanism, so that the feature information of the question and answer text is richer, andthe semantic matching accuracy of the question and answer text is improved.

Description

technical field [0001] The present invention relates to the technical field of natural language processing, in particular to a semantic matching method, device, medium and electronic equipment for question and answer text. Background technique [0002] At present, the method of semantic matching of Q&A text based on deep learning may include the following steps: a word embedding model trained based on neural network is used to represent the text as a word vector, which has stronger semantic representation ability. Model text by constructing long-short-term memory network LSTM (Long Short-Term Memory) or gated recurrent unit GRU (Gated Recurrent Unit) and other deep learning models. Although these methods are less dependent on feature selection and extract shallow semantic information and contextual local features to a certain extent, they cannot extract a large number of key global features and syntactic structure features, thus reducing the accuracy of semantic matching of ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the embodiments of the present invention is to provide a semantic matching method, device, medium, and electronic equipment for question-and-answer texts, so as to overcome the problem of low accuracy of semantic matching for question-and-answer texts at least to a certain extent

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