A Study of Modeling Algorithm Improvement for Semantic Analysis of Public English Texts

Semantics in public English texts are more challenging to understand accurately because they are influenced by specific contextual contexts. Traditional English text semantic understanding methods do not design their semantic understanding methods based on the conceptual semantic features of the text, and they have the problem of poor accuracy in understanding the deep semantics of English texts. For this reason, the article takes the public English text semantic algorithm as the research perspective, firstly conducts relevant theoretical research on English text semantic feature representation, then explores the text semantic extraction method based on the Dependency Tree-CRF, and deepens the understanding of English text semantics through the conceptualization and attention embedding methods. In the experiment of comparing the semantic coherence model with manual scoring, the experiment shows that by applying the semantic analysis model designed in this paper to the task of correcting the English writing of domestic college students and comparing it with the experimental results of manual scoring, it is found that the average absolute error between the scoring of the English compositions by this paper’s model and the scores of the compositions corrected by the teachers is 3.2051, i.e., the difference between the results of the manual correcting and the results of the correction by this paper’s model is It is not big, from which we can get that the model of this paper has good practical value.