Interactive-context-based natural language processing method

Abstract

The invention discloses an interactive-context-based natural language processing method. The method comprises the following steps of receiving first natural language input from a user, wherein the first natural language input comprises natural language input provided by not responding to question asking by the user; analyzing the first natural language input so that multiple key elements can be extracted from the first natural language input; storing the key elements; receiving second natural language input from the user; according to the second natural language input, selecting one or more key elements associated with the second natural language input from the multiple stored key elements; according to the second natural language input and the associated key elements, generating natural language output. According to the method, the mode that a robot obtains useful information can be expanded, and the scene interactive continuity can be ensured.

Description

technical field [0001] The present invention generally relates to the field of natural language processing, and in particular relates to a method for processing natural language based on interaction context. Background technique [0002] With the development of computer technology, artificial intelligence robots have begun to emerge in the market, such as Microsoft's "Xiaobing", Apple's "Siri", Baidu's "Durmi" and so on. These artificial intelligence robots are mainly divided into voice assistants and chats. The main function of the former is to provide users with useful information, while the latter aims to chat with users. No matter what kind of robot, it involves natural language processing technology. The natural language processing technology refers to processing the natural language input by the user so that the robot can understand the natural language. [0003] First of all, a major limitation of existing artificial intelligence robots is that these robots interac...

AI Technical Summary

Problems solved by technology

Due to the huge knowledge base, this leads to a huge amount of calculation, which greatly increases the calculation cost, and when encountering confusing expressions, such as "there is a Changchun pharmacy in Changchun City", the word segmentation system is also prone to errors

Regarding semantic determination, since the core vocabulary in the user's language input may have multiple meanings, for example, "potato" can refer to a kind of food or a "potato" video website, so when the existing robot encounters ambiguous words , it is easy to wrongly determine the semantics of the vocabulary

Inefficient and error-prone word segmentation and semantic determination lead to low efficiency and low accuracy of keyword extraction, resulting in low intelligence of robots and poor user experience

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