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Text classification toward a scientific forum
Zhang, Wen1; Tang, Xijin2; Yoshida, Taketoshi1
2007-09-01
Source PublicationJOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING
ISSN1004-3756
Volume16Issue:3Pages:356-369
AbstractText mining, also known as discovering knowledge from the text, which has emerged as a possible solution for the current information explosion, refers to the process of extracting non-trivial and useful patterns from unstructured text. Among the general tasks of text mining such as text clustering, summarization, etc, text classification is a subtask of intelligent information processing, which employs unsupervised learning to construct a classifier from training text by which to predict the class of unlabeled text. Because of its simplicity and objectivity in performance evaluation, text classification was usually used as a standard tool to determine the advantage or weakness of a text processing method, such as text representation, text feature selection, etc. In this paper, text classification is carried out to classify the Web documents collected from XSSC Website (http://www.xssc.ac.cn). The performance of support vector machine (SVM) and back propagation neural network (BPNN) is compared on this task. Specifically, binary text classification and multi-class text classification were conducted on the XSSC documents. Moreover, the classification results of both methods are combined to improve the accuracy of classification. An experiment is conducted to show that BPNN can compete with SVM in binary text classification; but for multi-class text classification, SVM performs much better. Furthermore, the classification is improved in both binary and multi-class with the combined method.
Keywordtext classification SVM BPNN Xiangshan Science Conference
DOI10.1007/s11518-007-5050-x
Language英语
Funding ProjectMinistry of Education, Culture, Sports, Science and Technology of Japan ; National Natural Science Foundation of China[70571078] ; National Natural Science Foundation of China[70221001]
WOS Research AreaOperations Research & Management Science
WOS SubjectOperations Research & Management Science
WOS IDWOS:000258569700007
PublisherSPRINGER HEIDELBERG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/4956
Collection中国科学院数学与系统科学研究院
Corresponding AuthorZhang, Wen
Affiliation1.Jap Adv Inst Sci & Technol, Sch Knowledge Sci, Tatsunokuchi, Ishikawa 9231292, Japan
2.Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Wen,Tang, Xijin,Yoshida, Taketoshi. Text classification toward a scientific forum[J]. JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING,2007,16(3):356-369.
APA Zhang, Wen,Tang, Xijin,&Yoshida, Taketoshi.(2007).Text classification toward a scientific forum.JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING,16(3),356-369.
MLA Zhang, Wen,et al."Text classification toward a scientific forum".JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING 16.3(2007):356-369.
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