Intelligent question-answering oriented sentence pair semantic matching method based on semantic feature map

Abstract

The invention discloses an intelligent question and answer oriented sentence pair semantic matching method based on a semantic feature map, and belongs to the technical field of artificial intelligence and natural language processing. The technical problem to be solved by the invention is how to capture more semantic context features, the relationship of coded information between different dimensions and the interaction information between sentences, and intelligent semantic matching of sentence pairs is realized. The adopted technical scheme is as follows: a sentence pair semantic matching model consisting of a multi-granularity embedding module, a deep semantic feature map construction network module, a feature conversion network module and a label prediction module is constructed and trained; and deep semantic feature graph representation of sentence information and two-dimensional convolutional encoding representation of semantic features are realized, and meanwhile, a final matching tensor of sentence pairs is generated through two-dimensional maximum pooling and attention mechanisms, and the matching degree of the sentence pairs is judged, so that the purpose of intelligent semantic matching of the sentence pairs is achieved. The device comprises a sentence pair semantic matching knowledge base construction unit, a training data set generation unit, a sentence pair semantic matching model construction unit and a sentence pair semantic matching model training unit.

Description

technical field [0001] The invention relates to the technical fields of artificial intelligence and natural language processing, in particular to a sentence-pair semantic matching method based on a semantic feature map for intelligent question-answering. Background technique [0002] The intelligent question answering system is one of the core technologies of human-computer interaction. It can automatically find matching standard questions in the question and answer knowledge base for the questions raised by users, and push the answers to the standard questions to users, which can greatly reduce the burden of manual answers. . Intelligent question answering system has a wide range of practical applications in self-service, intelligent customer service and other fields. For the vastly different questions raised by users, how to find matching standard questions for them is the core technology of the intelligent question answering system. The essence of this technology is to ...

AI Technical Summary

Problems solved by technology

However, since natural language sentences themselves belong to one-dimensional information, when applying convolutional neural networks, existing work usually only uses one-dimensional convolution kernels

Although the one-dimensional convolutional neural network can capture local information more effectively, it can only process sentences in one dimension, and cannot capture the interactive information between different dimensions; in fact, the information between different dimensions is not mutually exclusive. Independent; therefore, one-dimensional convolution also has the problem of missing some semantic information

To sum up, both the recurrent neural network and the one-dimensional convolutional neural network have disadvantages that cannot be ignored in the encoding and processing of sentence semantic information.

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