Rapid identification method and device for large-scale intention, and electronic equipment

Abstract

The invention provides a rapid identification method and device for large-scale intention, and electronic equipment. The method comprises the steps of performing semantic vector conversion on intention category information input by a conversation of a historical user; performing semantic clustering on each intention after semantic vector conversion; establishing an index for a result after semantic clustering, wherein the index is used for searching an intention category corresponding to dialogue input in a preset intention database; obtaining dialogue input of a current user in real time, andperforming search matching by using the index; and inputting the semantic vector and the search matching result input by the dialogue into a sorting model, and sorting to determine an intention recognition result. According to the method, a clustering index-deep matching recall-sorting scheme is adopted, so that the manpower is greatly saved, the thousand-level maximum-scale intention recognitionis realized, the intention recognition efficiency is improved, and the user intention recognition accuracy is also improved.

Description

technical field [0001] The invention relates to the field of computer information processing, in particular to a large-scale intent rapid identification method, device and electronic equipment. 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. Common dialogue systems include Siri, Echo, Bixby, Microsoft Xiaobing, Ali Xiaomi, smart speakers, etc. Intent recognition is the primary and important task in dialogue systems, especially in open dialogue scenarios, limited by the ability of the classifier, the dialogue is divided into dozens or hundreds of coarse-grained intentions, and the coarser granularity makes the chat Robots cannot accurately capture user intentions, which in turn affects the effect of human-computer interaction. [0003] Most of the types of intents defined by existing intent recognizers range from dozens to hundred...

AI Technical Summary

Problems solved by technology

But when the number of intent categories reaches tens of thousands or even hundreds of thousands, the multi-classifier based on softmax will fail, because the probability of assigning to each intent after softmax processing becomes very small and indiscriminate; while based on one- Although the method of combining multiple binary classifiers of vs-other can be expanded with the increase of the number of intents, the structure includes ten thousand binary classifiers, and the labor cost is basically unacceptable

In addition, the above two types of methods ignore the correlation information between intents

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