Research on Text Generation Model of Natural Language Processing Based on Computer Artificial Intelligence

This paper proposes a text generation system model for natural language processing based on artificial intelligence technology. In this model, pre-trained dynamic word vector ALBERT was used instead of the traditional BERT reference model for feature extraction to obtain word vectors. Through ontology, the factors that affect the semantic similarity of concepts, such as node density, node depth and node hierarchy order, are improved. The semantic distance, the relationship between concepts, the attribute of concepts and the level of concepts are considered comprehensively. The experiment on the public data set proves that the BLEU value and manual evaluation index of the algorithm on the test set are significantly improved compared with the baseline model. The effectiveness of the algorithm is proved by the system.