Simulation of English part-of-speech classification based on artificial intelligence and additive logistic regression

Abstract

English part-of-speech classification technology is a technology that can process text data, can effectively solve the problem of messy data in text information categories, make data structured and organized, and facilitate people to obtain effective information implicit in the text. This article transforms the original polynomial distribution into a generalized linear model and uses logistic regression algorithm for specific implementation. Moreover, the model proposed in this paper inherits the good explanatory characteristics of the decision tree, and it locally uses logistic regression to fit the data, which greatly improves the function space that logistic regression can fit. In addition, due to changes in the decision theory of logistic regression leaf nodes, the corresponding tree branch theory also needs to be changed accordingly. Finally, this paper designs experiments to study the performance of the model constructed in this paper. The research results show that the model constructed in this paper has high accuracy in the extraction and classification of English part-of-speech features.