Research on Spatial Conceptual Modeling of Natural Language Processing Based on Deep Learning Algorithms

Abstract

From the point of view of computer science, especially artificial intelligence, the task of natural language understanding is to establish a computer model. This computer model can give the result of understanding natural language like human. An important aspect of natural language understanding lies in how to express knowledge to the computer, how to express knowledge, and how to establish the connection and reasoning between knowledge, that is, how to apply the brain’s association, reasoning and selection process to the model of language processing. The physical structure and logical structure of modern computers are very clear, but what we need is a set of feasible formal thinking mechanism to enable machines to process natural language information. Deep learning is one of the areas of machine learning that is close to AI. It is analyzed by simulating human brain learning nerves. Deep learning is derived from the study of artificial neural networks and is a structure for learning deep nonlinear networks. By presenting complex function approximations, the input data is distributed and represented, and the ability of the data samples to focus on the essential characteristics of the data set is revealed.