Man-machine conversation understanding method and system for specific field and relevant equipment

Abstract

The invention relates to the field of artificial intelligence, in particular to a man-machine conversation understanding method and system for a specific field and relevant equipment and aims to improve accuracy of conversation understanding. The conversation understanding method of the man-machine conversation system comprises the steps as follows: receiving a word currently input by a user and mapping the word to a vector space; expressing a historical word vector, semantic annotation information and intent category information as vectors by using a semantic representation layer; acquiring asemantic label of the current word by using the semantic labeling layer; acquiring the intent category of the current word by using an intent recognition layer. Extra part-of-speech information is introduced during model training, the part-of-speech prediction layer is used for predicting the part-of-speech of the next input word, semantic information shared by semantic annotations, intent recognition and part-of-speech prediction is fully used and promoted mutually by joint processing of the three tasks; the system and the method have clear logic, high efficiency and high accuracy, and the technical problem that the existing man-machine conversation system cannot effectively perform real-time conversation understanding is properly solved.

Description

technical field [0001] The present invention relates to the field of artificial intelligence, in particular to a method and system for understanding human-machine dialogues in a specific field and related equipment. Background technique [0002] Dialogue understanding is an important task in human-computer dialogue systems, including two subtasks of semantic annotation and intent recognition. Semantic labeling refers to assigning a semantic label to each word in the user input sentence, and intent recognition refers to judging the intent of each user input sentence. [0003] Most of the user input sentences in the human-computer dialogue system are spoken language, and they are relatively short, and have the characteristics of variable and inconsistent sentence forms, which makes semantic annotation and intent recognition particularly difficult. [0004] Existing technologies mainly include the following: [0005] (1) Traditional machine learning method: This method regard...

AI Technical Summary

Problems solved by technology

However, this method is still limited to using textual information input by the user, resulting in the underutilization of the semantic information shared between the two tasks.

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