Intention recognition method and device based on multi-parameter K-means algorithm and electronic equipment

Abstract

The invention provides an intention recognition method and device based on a multi-parameter K-means algorithm and electronic equipment. The method comprises the following steps: establishing a sampledata set, wherein the sample data set comprises a plurality of semantic vectors obtained by conversion of dialogue texts; performing multiple rounds of clustering processing on the sample data set byusing the K-means algorithm, adopting different k values for each round of clustering, and outputting an initial clustering result; setting a fusion strategy, determining an initial clustering resultto be fused, wherein the fusion strategy comprises fusion parameters and fusion rules; according to the fusion rule, performing fusion processing on the initial clustering result to be fused to forma final clustering result; and based on the final clustering result, performing intention recognition on the voice input by the current user dialogue. According to the method, the improved K-means algorithm is adopted, more accurate intention classification and recognition are achieved, the intention clustering quality is improved, and the method is optimized.

Description

technical field [0001] The invention relates to the field of computer information processing, in particular to an intention recognition method, device and electronic equipment based on a multi-parameter K-means algorithm. Background technique [0002] With the development of Internet technology, dialogue systems have been widely used in e-commerce, smart devices, etc., and have attracted more and more attention. Intent recognition is the primary and important task in the dialogue system, and it is a multi-classification problem. Correspondingly, a multi-classification model needs to be used for corresponding processing. In fact, the classification problem is the basic problem of machine learning, and there are regression, clustering and so on. [0003] Clustering is to classify existing data objects, so that the similarity between each category is as large as possible, and the similarity between each category is as small as possible. In general, in the process of dividing ...

AI Technical Summary

Problems solved by technology

However, there is still much room for improvement in terms of applied clustering quality and method optimization

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