Studying Intelligent Testing Algorithms for English Writing Using Neural Networks and English Semantics
Liyue Zhang, Zhiyu Wang · 2023
This study aims to explore an intelligent testing algorithm for English writing based on English semantics and neural networks. By combining semantic analysis and advanced neural network technology, we try to develop a smarter and more accurate English writing test to more comprehensively assess students' language use and expression. First, we use English semantic analysis to dig deeper into the semantic structure of sentences and paragraphs. This includes an understanding of the semantic relationships of words, phrases and sentences, as well as a synthesis of contextual information. Through this foundation of semantic analysis, we are able to more accurately capture and assess students' linguistic expression in writing. Second, we introduce advanced neural network technology, especially deep learning models suitable for natural language processing tasks. By building neural network models, we aim to enable our algorithms to learn and understand syntactic and semantic patterns in large-scale corpora. This deep learning approach is expected to make our algorithms for intelligent testing of English writing more adaptive and generalisable, capable of handling a wide range of language styles and topics. Finally, we apply the studied algorithms to real-world English writing test scenarios and evaluate their performance through comparative experiments. Our goal is to provide a more comprehensive and intelligent assessment method that not only examines grammatical accuracy but also focuses on semantic coherence and expressiveness. Through this research, we expect to introduce more advanced intelligent assessment tools to the field of English writing education and improve the accuracy and usefulness of the test.