Optimization of Intelligent Corpus and Language Writing Teaching Based on Embedded Task Processing System

The study designs a text processing system based on text embedding model for use in language writing teaching aid. After analyzing the advantages and disadvantages of each text embedding model, the neural network word embedding model was chosen to construct word embedding vectors. After designing and implementing the text processing system, an intelligent corpus for language writing teaching is generated. The text processing system and intelligent corpus are utilized in the teaching of language writing. The changes in language writing levels of the experimental group and the control group are compared through the teaching experiment method, so as to compare the application effects of the text processing system and the intelligent corpus of this paper with those of conventional language writing teaching. It has been found that the text categorization performance proposed in this paper is significantly better than other text representation methods and has the highest text representation capability. Before the experiment, there were no significant differences between the language writing levels of the experimental group and the control group. After the experiment, the experimental group’s language writing dimensions and overall level were better than the control group’s high level, and the p-value was significant. At the same time, we also conducted a practical teaching experiment, and found that the experimental group of students’ language writing level increased by a large margin, with a p-value of less than 0.01. The improvement in dimensions of the control group was very small, and the difference did not show any significance. Compared to traditional language writing teaching methods, the text processing system and intelligent corpus designed in this paper can effectively improve students’ language writing level.