Study on Intelligent Scoring of English Composition Based on Machine Learning from the Perspective of Natural Language Processing

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ACM Transactions on Asian and Low-Resource Language Information Processing Pub Date : 2024-06-04 DOI:10.1145/3625545
Jing Tang
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引用次数: 0

Abstract

Knowledge management is crucial to the teaching and learning process in the current era of digitalization. The idea of "learning via working together" is making Natural Language Processing a popular tool to improve the learning process based on the intelligent system for evaluating the composition. English language learning is highly dependent on the composition written by the students under various topics. Teachers are facing huge difficulties in the evaluation of the composition as the level of writing by the students will vary for individual. In this research, Natural Language Processing concept is utilized for getting trained with the student's writing skills and Multiprocessor Learning Algorithm (MLA) combined with Convolutional Neural Network (CNN) (MLA-CNN) for evaluating the composition and declaring the scores for the students. The model's composition scoring rate is validated using a range of learning rate settings. Some theoretical notions for smart teaching are proposed, and it is hoped that this automatic composition scoring model would be used to grade student writing in English classes. When applied to the automatic scoring of students' English composition in schools, the suggested composition scoring system trained by the MLP-CNN has great performance and lays the groundwork for the educational applications of ML inside AI. The study results proved that the proposed model has provided an accuracy of 98%.

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自然语言处理视角下基于机器学习的英语作文智能评分研究
在当前的数字化时代,知识管理对教学过程至关重要。通过合作学习 "的理念正使自然语言处理成为一种流行的工具,用于改进基于智能系统的作文评价的学习过程。英语学习在很大程度上依赖于学生根据不同主题所写的作文。由于学生的写作水平因人而异,教师在作文评价方面面临巨大困难。本研究利用自然语言处理概念对学生的写作技巧进行训练,并结合多处理器学习算法(MLA)和卷积神经网络(CNN)(MLA-CNN)对学生的作文进行评估和打分。该模型的作文评分率通过一系列学习率设置进行了验证。提出了一些智能教学的理论概念,并希望该自动作文评分模型能用于英语课堂的学生作文评分。在应用于学校学生英语作文的自动评分时,建议的由 MLP-CNN 训练的作文评分系统表现出色,为人工智能中的 ML 在教育领域的应用奠定了基础。研究结果证明,所提出的模型准确率高达 98%。
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来源期刊
CiteScore
3.60
自引率
15.00%
发文量
241
期刊介绍: The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.
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