Man-machine interaction method and device based on semantic net and intention recognition and medium

Abstract

The invention discloses a man-machine interaction method based on a semantic net and intention recognition, relates to the field of natural language processing, and aims to achieve accurate recognition of question intentions and improve the question and answer quality of man-machine interaction. The method comprises the following steps: obtaining common question answers in the industry as an interactive data source; performing semantic annotation on standard questions in the common question solutions, and constructing an industry semantic network; obtaining a training corpus; training an intention recognition classification model through the training corpus; and receiving a user question, performing intention recognition on the user question through the intention recognition classificationmodel to obtain an intention candidate set, performing multiple rounds of man-machine interaction through the industry semantic network based on the intention candidate set, determining a standard question matched with the intention of the user question, and outputting an answer. The invention further discloses electronic equipment and a computer storage medium.

Description

technical field [0001] The invention relates to the field of natural speech processing, in particular to a human-computer interaction method, device and medium based on semantic web and intention recognition. Background technique [0002] Personnel in call centers or customer service centers are generally highly mobile, which leads to high training costs for enterprises and a decline in customer service satisfaction, resulting in a substantial increase in operating costs. Therefore, more and more attention is paid to intelligent customer service. However, in the interaction process, intelligent customer service faces problems such as inaccurate recognition of intentions, vague intentions that cannot be located, and high maintenance costs for training corpus. [0003] In order to solve the above problems, in the prior art, there is a Chinese patent application 201710575327.5, which discloses a question answering method and device based on knowledge graphs, which acquire natur...

AI Technical Summary

Problems solved by technology

However, this patent application requires a knowledge map constructed from a large amount of corpus, and the current general entity recognition model can only recognize names of people, places, and institutions, etc., and lacks training corpus for the recognition of professional entities in specific fields, resulting in a lack of applicability for question and answer in specific fields , and there are many colloquial sentences in the question-and-answer process, resulting in the inability to extract entities and attributes, so the problem cannot be accurately located

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