Man-machine interaction question-answering method and system based on complex intention intelligent identification

Abstract

The invention discloses a man-machine interaction question-answering method and system based on complex intention intelligent recognition, and the method comprises the steps: obtaining an original question sentence of a user, carrying out the sentence segmentation and part-of-speech tagging, and obtaining the part-of-speech information of each component word of the question sentence; performing dependency syntax analysis on the question sentence to obtain a dependency syntax tree; carrying out industry entity identification to obtain industry entities and the number, and extracting a core dependency tree to simplify questions; carrying out industry question relation classification on the questions, carrying out Chinese multi-intention question rewriting, and then carrying out knowledge retrieval on the questions; and selecting and generating answers for knowledge retrieval results, and returning the answers to the user. According to the method and system, multi-intention complex questions can be effectively simplified in any industrial scene, the intention of the user can be accurately understood, the industrial knowledge can be more naturally fed back to the user, the user can more accurately and quickly obtain the required industrial knowledge, the user experience is improved, and the method and system are particularly suitable for man-machine interaction intelligent questions and answers in the medical industry.

Description

technical field [0001] The invention relates to human-computer interaction intelligent question answering technology, in particular to a human-computer interaction question answering method and system based on complex intention intelligent recognition. Background technique [0002] With the continuous development of Web technology, people are gradually moving from the traditional "Web 1.0" era centered on document interconnection and "Web 2.0" era characterized by data interconnection to the new "Web 3.0" era based on knowledge interconnection. The emergence of knowledge graph (Knowledge Graph, KG) has brought the dawn of knowledge interconnection in the era of "Web3.0". Together, they form a knowledge network with a unified structure and clear order, and play a huge application value in intelligent question answering, intelligent search, and intelligence analysis. The knowledge map is essentially a structured semantic network knowledge base. The basic unit is the triplet f...

AI Technical Summary

Problems solved by technology

[0006] To sum up, the main problems in the knowledge graph question answering system of related technologies: (1) Most knowledge question answering adopts the template matching method, and the decomposition of complex questions is to perform probability calculation on a large number of pre-established question templates

(2) In the field of intelligent question answering, the deep learning model has attracted much attention due to its powerful learning and feature extraction capabilities, but requires a large amount of training corpus resources

Due to the lack of large-scale open source Q&A corpus data in the medical field, it generally requires a lot of manpower and material resources to collect data from the Internet or formulate question templates to generate data. precise

(3) The intention of the questions in the actual environment is complex, and the existing question answering system in the medical field does not effectively mine the grammatical structure and entity relationship of the questions, resulting in incomplete, wrong or matching failures in the answers retrieved from the knowledge base

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