{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:42:33Z","timestamp":1777704153422,"version":"3.51.4"},"reference-count":18,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,3,2]]},"abstract":"<jats:p>Chinese fill-in-the-blank questions contain both objective and subjective characteristics, and thus it has always been difficult to score them automatically. In this paper, fill-in-the-blank items are divided into those with word-level or sentence-level granularity; then, the items are automatically scored by different strategies. The automatic scoring framework combines semantic dictionary matching and semantic similarity calculations. First, fill-in-the-blank items with word-level granularity are divided into two types of test sites: the subject term test site, and the common word test site. We propose an algorithm for identifying an item\u2019s test site. Then, a subject term dictionary with self-feedback learning ability is constructed to support the scoring of subject term test sites. The Tongyici Cilin semantic dictionary is used for scoring common word test sites. For fill-in-the-blank items with sentence-level granularity, an improved P-means model is used to generate a sentence vector of the standard answer and the examinee\u2019s answer, and then the semantic similarity between the two answers is obtained by calculating the cosine distance of the sentence vector. Experimental results on actual test data show that the proposed algorithm has a maximum accuracy of 94.3% and achieves good results.<\/jats:p>","DOI":"10.3233\/jifs-202317","type":"journal-article","created":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T15:42:22Z","timestamp":1610120542000},"page":"5473-5482","source":"Crossref","is-referenced-by-count":4,"title":["Automatic scoring of Chinese fill-in-the-blank questions based on\u00a0improved P-means"],"prefix":"10.1177","volume":"40","author":[{"given":"Dong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mathematics and Big Data, Guizhou Education University, Guiyang, China"},{"name":"Big Data Science and Intelligent Engineering Research Institute, Guizhou Education University, Guiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mathematics and Big Data, Guizhou Education University, Guiyang, China"},{"name":"Big Data Science and Intelligent Engineering Research Institute, Guizhou Education University, Guiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Mathematics and Big Data, Guizhou Education University, Guiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Zuo","sequence":"additional","affiliation":[{"name":"School of Mathematics and Big Data, Guizhou Education University, Guiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"S2","key":"10.3233\/JIFS-202317_ref1","first-page":"102","article-title":"Automated Scoring Chinese Subjective Responses Based on Improved-LDA[J]","volume":"44","author":"Haijiao","year":"2017","journal-title":"Computer Science"},{"key":"10.3233\/JIFS-202317_ref2","unstructured":"Xianwu C. and Daobo L. , Research on automatic marking system of 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Zhengzhou University(Natural Science Edition)"},{"key":"10.3233\/JIFS-202317_ref12","unstructured":"Qun L. , Sujian L. , Word Similarity Computing Based on How-Net[C], In: Proceedings of the 3th Conference on Word Semantic, Taipei, (2002)."},{"issue":"6","key":"10.3233\/JIFS-202317_ref14","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1007\/BF02960786","article-title":"Semanticcomputation in Chinese question-answering system[J]","volume":"17","author":"Sujian","year":"2002","journal-title":"Journalof Computer Science and Technology"},{"key":"10.3233\/JIFS-202317_ref15","first-page":"1","article-title":"Efficient Estimation of Word Representations in Vector Space [C]\/\/","volume":"2013","author":"Mikolov","journal-title":"International Conference on Learning Representations"},{"key":"10.3233\/JIFS-202317_ref16","first-page":"1532","article-title":"Glove: Global Vectors for Word Representation[C]\/\/","volume":"2014","author":"Pennington","journal-title":"Conference on Empirical Methods in Natural Language Processing"},{"key":"10.3233\/JIFS-202317_ref17","doi-asserted-by":"crossref","unstructured":"Joulin A. , Grave E. , Bojanowski P. , et al., Bag of Tricks for Efficient Text Classification[C]\/\/Con-ference of the European Chapter of the Association for Computational Linguistics (2017).","DOI":"10.18653\/v1\/E17-2068"},{"key":"10.3233\/JIFS-202317_ref20","unstructured":"Yi S. , Hangping Q. and Ruizhi K. , A Sentence Embedding Model Based On Cut words by Variance Weight-Smooth Inverse Frequency[J], Computer Engineering 45(09) (2019), 204\u2013210+234."},{"key":"10.3233\/JIFS-202317_ref27","unstructured":"Arora S. , Liang Y. and Ma T. , A simple but tough to beat baseline for sentence embeddings[C]\/\/In International Conference on Learning Representations, (2017)."},{"issue":"04","key":"10.3233\/JIFS-202317_ref30","first-page":"104","article-title":"Short text classficationbased on synonymy expansion[J]","volume":"41","author":"Dong","year":"2015","journal-title":"Journal of Lanzhou University of Technology"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-202317","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:40:35Z","timestamp":1777455635000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-202317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,2]]},"references-count":18,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/jifs-202317","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,2]]}}}