Natural language semantic analysis system and method based on depth neural network

Abstract

The invention discloses a natural language semantic analysis system and method based on the depth neural network. The method comprises the steps that a knowledge map is built, a training set is inputted, and an N-Gram probability model is obtained, a matrix is obtained as an input by representing words as vectors using the word2vec, a deep belief network model is used for the entity identification and the input validation set, the classifier parameters and the input test set are adjusted, the group abilities of the models are tested, the knowledge graph method is adopted to apply reasoning to the entities in the descriptions of the language, and corresponding conclusions are obtained. Compared with the prior art, the natural language semantic analysis system and method based on the depth neural network uses the knowledge graph method to apply reasoning to the entities in the descriptions of the language and to obtain the corresponding conclusions, so that our natural language understanding abilities are provided not only with the capacity to understand the literal meaning, but also with logical reasoning and the understand of the meaning on a deep level, and the method has promotable and practical value.

Description

technical field [0001] The invention relates to a new field of machine learning research, in particular to a natural language semantic analysis system and method based on a deep neural network. Background technique [0002] Deep learning has made great achievements in the fields of image and speech processing, but in the natural language processing tasks that are also in the category of human cognition, research has not yet achieved major breakthroughs. Different from speech and images, the "data source" used for initial input in "natural language" in deep learning is words or words, which already contain human semantic interpretation and are formed after human subjective thinking and processing. Essentially, human language Understanding is a complex knowledge reasoning process. However, current deep learning focuses too much on "automatic learning", resulting in the processing of natural language mostly still staying in the understanding of "shallow semantics". The present ...

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Problems solved by technology

However, the current deep learning focuses too much on "automatic learning", resulting in most

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