Textual similarity calculation method and device, and intelligent robot

Abstract

Embodiments of the invention provide a textual similarity calculation method and a textual similarity calculation device, and an intelligent robot. The embodiments of the invention comprise the stepsof: first obtaining the longest common subsequences of two texts, then calculating the intersection and the union of the vocabulary sets corresponding to the two texts, and calculating according to the obtained intersection and union to obtain a first similarity, calculating a second similarity by using the vocabulary sets corresponding to the longest common subsequences and the previously obtained union, and finally calculating according to the first similarity and the second similarity to obtain a target similarity of the two text. The above technical solution combines the longest common subsequences and each vocabulary in the text to calculate the similarity of the two texts, thereby effectively improving the calculation accuracy of the text similarity. Progressively, chat robots or intelligent robots may provide users with more accurate answers by using accurate text similarity, which improves the service quality of the chat machines or intelligent robots, and the user experience.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of text processing, and more specifically, relate to a text similarity calculation method and device, and an intelligent robot. Background technique [0002] Chatbot is a popular application driven by big data and artificial intelligence technology. During use, the user enters the chat content, that is, the user enters the question raised by the user, and the chatbot automatically generates the corresponding reply according to the question entered by the user. and give feedback to users. This artificial intelligence processing method can greatly improve service efficiency and user experience. There are currently many types of chatbots, such as Apple’s Siri, Microsoft’s Cortana and Xiaoice, Baidu’s Dumi, and Jingdong’s JIMI (JD, Instant Messaging Intelligence), and many others Chatbots, such as children's educational robots, vehicle control robots, etc. [0003] In the actual applicat...

AI Technical Summary

Problems solved by technology

The problem is that each individual vocabulary cannot accurately express the original meaning of the corresponding text, which results in inaccurate similarity between the texts calculated using each vocabulary, for example, there are two texts: I like you and you Like me, the meanings of these two texts are completely different, but the vocabulary after word segmentation of the two texts is exactly the same, then the similarity of the two texts calculated by using the existing technology is 1, which is obviously inaccurate

Furthermore, since the calculation of text similarity in the prior art is not accurate enough, the replies that the chat robot pushes for the user based on the text similarity must not be accurate enough, which seriously affects the service quality of the chat robot and the user experience.

Images