{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T04:27:17Z","timestamp":1741753637527,"version":"3.38.0"},"reference-count":15,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,2,20]]},"abstract":"<jats:p>Objective:In order to save teachers\u2019 correcting time, improve the accuracy and efficiency of English composition grading.Methods: This paper briefly introduces the algorithm of deep sentence smoothness and text semantic matching based on graph neural network, and then designs an automatic scoring algorithm for English text. Result: The experimental data was collected from 12,000\u00a0essays written by international students in the United States in the Pratt &amp; Whitney Foundation\u2019s Automated Student Value Assessment Project (ASAP), and these data were graded through a comparative experiment,Through comparative tests, the automatic scoring algorithm designed in this paper can achieve better scoring results and better handle automatic essay scoring problems. Among all the experimental mean values of evaluation methods, the experimental mean value of the algorithm designed in this paper is 0.790, the smoothness algorithm is 0.768, and the text matching vector is 0.759. The experimental mean values of the other two traditional automatic scoring algorithms are 0.710 and 0.712\u00a0respectively, and the results are lower than the algorithm designed in this paper. Conclusion: According to the experimental results, it can be concluded that good feature selection can give good scoring performance to the algorithm and cope with the problem of automatic scoring. At the same time, it also confirms the feasibility of the algorithm designed in this paper, which can be effectively applied in practical English composition scoring.<\/jats:p>","DOI":"10.3233\/idt-230305","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T16:05:23Z","timestamp":1705421123000},"page":"397-406","source":"Crossref","is-referenced-by-count":0,"title":["Performance comparison of multiple scoring algorithms: A study of automatic scoring of English text"],"prefix":"10.1177","volume":"18","author":[{"given":"Lingling","family":"Hu","sequence":"first","affiliation":[]}],"member":"179","reference":[{"issue":"12","key":"10.3233\/IDT-230305_ref1","first-page":"42","article-title":"Peer Assessment Teaching Practice of College English Writing based on Automatic composition assessment system [J]","volume":"44","author":"Wu","year":"2022","journal-title":"Journal of Huzhou Teachers College"},{"issue":"00","key":"10.3233\/IDT-230305_ref2","first-page":"219","article-title":"The application of automatic writing assessment in the Wisdom classroom of academic English Writing: a case study of learners [J]","author":"Chen","year":"2022","journal-title":"Research in Educational Linguistics"},{"issue":"33","key":"10.3233\/IDT-230305_ref3","first-page":"94","article-title":"The reliability of automatic essay scoring System and its implications for College English Writing Teaching: A comparative analysis of iWrite Scoring System and Manual scoring [J]","volume":"8","author":"Luan","year":"2002","journal-title":"Journal of Higher Education"},{"issue":"04","key":"10.3233\/IDT-230305_ref4","first-page":"133","article-title":"The definition of sub-item scoring criteria for foreign language writing test from the perspective of raters [J]","author":"Zou","year":"2022","journal-title":"Contemporary Foreign Language Studies"},{"key":"10.3233\/IDT-230305_ref5","unstructured":"Shi F. Application of Word2Vec word vector based on natural language Processing [J]. Journal of Heihe University. 2019; 11(07): 173-177."},{"issue":"03","key":"10.3233\/IDT-230305_ref6","first-page":"71","article-title":"Text sentiment tendency analysis based on neural network under big data [J]","volume":"44","author":"Wei","year":"2023","journal-title":"Software"},{"issue":"07","key":"10.3233\/IDT-230305_ref7","first-page":"1400","article-title":"Text topic modeling of Skip-Gram structure and word embedding Characteristics [J]","volume":"41","author":"Xia","year":"2020","journal-title":"Minicomputer Systems"},{"issue":"07","key":"10.3233\/IDT-230305_ref8","first-page":"1372","article-title":"Research on text classification based on improved CBOW and BI-LSTM-ATT [J]","volume":"49","author":"Wang","year":"2019","journal-title":"Computer and Digital Engineering"},{"issue":"08","key":"10.3233\/IDT-230305_ref9","first-page":"99","article-title":"Internal Threat Detection Method Based on Hybrid N-Gram Model and XGBoost Algorithm [J]","author":"Sun","year":"2022","journal-title":"Computer and Modernization"},{"issue":"07","key":"10.3233\/IDT-230305_ref10","first-page":"1471","article-title":"Incremental outdoor scene discovery based on hierarchical word bag model [J]","volume":"37","author":"Chen","year":"2020","journal-title":"Control Theory and Application"},{"issue":"1","key":"10.3233\/IDT-230305_ref11","first-page":"79","article-title":"Automatic scoring of subjective questions based on doc2vec [J]","volume":"28","author":"Xiao","year":"2020","journal-title":"Modern Computer"},{"issue":"S1","key":"10.3233\/IDT-230305_ref12","first-page":"221","article-title":"Research on Long Text Topic Clustering Based on Doc2Vec Enhanced Features [J]","volume":"50","author":"Chen","year":"2023","journal-title":"Computer Science"},{"issue":"11","key":"10.3233\/IDT-230305_ref13","first-page":"83","article-title":"Patch Verification Method Combining Doc2Vec and BERT Embedding Technology [J]","volume":"49","author":"Huang","year":"2022","journal-title":"Computer Science"},{"issue":"6","key":"10.3233\/IDT-230305_ref14","first-page":"53","article-title":"Named Entity Recognition Method based on Multi-network feature Extraction [J]","author":"Wang","year":"2023","journal-title":"Computer Programming Skills and Maintenance"},{"issue":"11","key":"10.3233\/IDT-230305_ref15","first-page":"135","article-title":"Research on Agricultural Entity Naming Recognition by Integrating Multiple Feature Words Embedding [J]","volume":"43","author":"Ding","year":"2023","journal-title":"Modern Intelligence"}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDT-230305","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T08:58:30Z","timestamp":1741683510000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDT-230305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,20]]},"references-count":15,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/idt-230305","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"type":"print","value":"1872-4981"},{"type":"electronic","value":"1875-8843"}],"subject":[],"published":{"date-parts":[[2024,2,20]]}}}