A clustering pre-analysis-based multi-intention recognition and semantic slot filling combined modeling method

Abstract

The invention provides a clustering pre-analysis-based multi-intention recognition and semantic slot filling combined modeling method. The method comprises the following steps: acquiring a multi-intention text input by a current user in real time and preprocessing the multi-intention text; constructing a multi-intention recognition model based on clustering pre-analysis, wherein the multi-intention recognition model is used for recognizing a plurality of intentions of the user; building a BiLSTM-CRF semantic slot filling model based on a Slot-Gated association gate mechanism, and guiding filling of semantic slots by fully utilizing an intention recognition result; and optimizing the constructed joint model of multi-intention recognition and semantic slot filling. According to the invention, the relation between intention recognition and semantic slot filling is fully considered, a joint modeling method is provided, two semantic analysis subtasks are combined into one semantic analysis task, the accuracy of semantic slot filling is improved while the accuracy of multi-intention recognition is improved, and therefore the quality of natural language semantic analysis is improved; in practical application, the human language understanding capability of a machine in man-machine conversation can be effectively improved, and the problem solving capability and the man-machine conversation experience are improved.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to a natural language semantic analysis method in a man-machine dialogue system. Background technique [0002] With the rapid development of artificial intelligence, people's requirements for the intelligence of many application scenarios are increasing. To meet the requirements of intelligence, good human-computer interaction is essential. At present, there are various ways of human-computer interaction, among which the most convenient way is to use natural language. Therefore, the voice of using self-speaking language to realize human-computer dialogue is getting higher and higher. The human-computer dialogue system has received extensive attention from academia and industry, and has a very wide range of application scenarios. [0003] To realize the human-computer dialogue system, the natural language semantic analysis technology is indispensable. The quality of seman...

AI Technical Summary

Problems solved by technology

However, the traditional separate modeling method does not fully consider the connection between the two tasks, so that the semantic information cannot be effectively used.

In addition, human-computer dialogue systems often face the problem of multi-intent recognition, that is, the intention text entered by the user may not only contain one intention, but may also appear in multiple intentions.

At present, the research on intent recognition mainly focuses on the recognition of single intent. Compared with single intent recognition, multi-intent recognition is not only more complicated to recognize but also requires a higher degree of semantic understanding.

Images