A question-answer matching method based on deep learning

Abstract

The invention relates to a deep learning-based question and answer matching method. The method comprises the following steps of: 1) sufficiently learning word orders and sentence local features of a question text and an answer text by utilizing two underlying deep neural networks: a long short-term memory network LSTM and a convolutional neural network CNN; and 2) selecting a keyword with best semantic matching on the basis of a pooling manner of an attention mechanism AM. Compared with existing methods, the method has the advantages of being in low in feature engineering workload, strong in cross-field performance and relatively high in correctness, and can be effectively applied to the fields of commercial intelligent customer service robots, automatic driving, internet medical treatment, online forum and community question answering.

Description

technical field

[0001] The present invention relates to the field of computer application technology, in particular to a question-answer matching technology based on deep learning. Background technique

[0002] The intelligent question answering system mainly solves the real intention analysis of questions, the matching relationship between questions and answers, understands user questions described in natural language, and returns concise and accurate matching correct answers by searching heterogeneous corpora or question and answer knowledge bases . The processing framework of the question answering system includes three components: question understanding, information retrieval, and answer generation. According to the data domain of user questions, question answering systems can be divided into question answering systems for limited domains, question answering systems for open domains, and question answering systems for frequently asked questions (FAQ). The present inven...

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