Head-Driven English Syntactic Translation Model Based on Natural Language Processing

Natural language is an important symbol of human intelligence, so making computers understand natural language is one of the fundamental tasks of artificial intelligence research. The study of English syntactic analysis has important theoretical value and urgent practical needs. In this paper, a head-driven English syntactic translation model based on natural language processing is designed, which integrates automatically acquired semantic classes into the syntactic analysis model to solve the problem of data sparseness in the model with small samples. Using a semantic dictionary and applying the principle of minimum description length, a semantic class with granularity between parts of speech and vocabulary is automatically determined for the syntactic analysis model. In English chunk analysis task, the algorithm improves the accuracy of chunk analysis, and it can also be applied to other natural language processing tasks or other classification problems.