English Syntactic Analysis and Word Sense Disambiguation Strategy of Neutral Set from the Perspective of Natural Language Processing

In order to improve the effect of English semantic analysis, under the support of natural language processing, this paper analyzes English syntactic analysis and the word sense strategy of the neutral set and solves the parameters through data training, so as to solve the probability distribution of the maximum entropy model of each order. Moreover, by comparing the prediction probability of the model to the judgment mode with the experimental data, it is found that the first-order maximum entropy model (independent model) is quite different from the data. Therefore, when judging data in English semantics, we cannot only consider the influence of second-order correlations but should also consider higher-order correlations. The research results of the simulation experiment show that the English syntactic analysis and the word sense disambiguation strategy of the neutral set proposed in this paper from the perspective of natural language processing are very effective.