Social-scene construction method and system based on language environment

Abstract

Aiming at shortcomings of scene construction methods and systems of prior social tools, a scene robot is used to carry out natural language understanding (NLU) according to conversation contents between conversation subjects to obtain conversation language-environment information, then a database is associated for acquiring data, medias\or a virtual reality technology\or an augmented reality technology is used to generate a scene, and transition from a language environment to the scene is realized; and the generated scene is fused with a conversation\or exists in time and space in an associated and dependent manner, so that the conversation scene of a social tool is constructed, understanding of people on social-language semantics is enhanced, and the expressive force and the sentiment of the conversation contents are improved.

Description

technical field [0001] The present invention belongs to H04L12 / 58(2006.01)I. Background technique [0002] The invention belongs to a context-based social scene construction method and system. Social tools connect people and life. The main representative products include WeChat, Momo, qq, Weibo, Yixin, Twitter, Facebook, G+, whatsapp, yy , line, msn, Aliwangwang, Michat, Weiju, Tieba, Laijing, Hug, Maimai, etc. The scene construction of these social tools adopts a variety of methods, except for WeChat and QQ, which have ""magic emoji"" interesting emoticons In addition to the methods and systems for constructing scenes, most of them use fixed theme background pictures, and the scene construction characteristics of existing social tools are that chat participants constitute the main body of the scene, and the language and images cannot be easily expressed. Abstract, informative, and behavioral content lacks expressiveness, such as Figure 4 As shown, one party (301) of the c...

AI Technical Summary

Problems solved by technology

[0009] The existing social software scene construction methods and systems have the following disadvantages: 1. The main body of the scene is the chat participant, which lacks expressiveness to the content of knowledge, abstraction and language behavior that cannot be simply expressed by language and images

2. Only analyze the conversation content of one party in the conversation, and generate social scenes through methods such as preset keyword matching, but fail to analyze the context content to obtain contextual information

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