Semantic understanding processing model based on machine learning

Abstract

Machine learning and artificial intelligence have become very diverse, and progress h as been made in perception technology, which can recognize the senses of machines, including hearing, vision, smell, taste and touch. We are recently studying a technological change that ha s grown the dialogue information in our lives into a mainstream recognizable digital activity. T his project will build a conversation subject model in the computing framework, which tracks t he conversation speech behavior by the functional grammar model. The recognition of Machin e Cognition and logic language is the key path of our research. The language model used is Rol e and Reference Grammar (RRG) to decompose sentences and understanding. In addition, we find an optimization scheme to assist the main body thinking is internal effect and external effect. (1) The subject has its own emotions (anger, joy, sadness, disappointment, joy), which c an affect the subject’s state at any time. (2) the external effect of the subject is determined by pe ople and other subjects, and the intelligent conversation agent will get interaction and composit e response through two effects. The RRG directional link system will be used in the language f ramework to build a language model in the software to help the machine analyze human langua ge through speech behavior. This interactive language analysis model ensures that the machine can accurately define human meaning in intelligent conversation. In this project, we will impro ve the development of machine learning and artificial intelligence in human language recogniti on and analysis, and the technology is very useful in various human behaviors such as smart ho me, enterprise management and data analysis. There is no doubt that this technology will make a great contribution to human beings in the field of Computing Science in the future.