Text Classification Using Sentential Frequent Itemsets

Text classification techniques mostly rely on single term analysis of the document data set, while more concepts, especially the specific ones, are usually conveyed by set of terms. To achieve more accurate text classifier, more informative feature including frequent co-occurring words in the same s...

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Bibliographic Details
Published inJournal of computer science and technology Vol. 22; no. 2; pp. 334 - 337
Main Authors Liu, Shi-Zhu, Hu, He-Ping
Format Journal Article
LanguageEnglish
Published Beijing Springer Nature B.V 01.03.2007
College of Computer Science, Huazhong University of Science and Technology, Wuhan 430071, China
Subjects
Classification
Classifiers
Conveying
Data mining
Documents
Mining
Sentences
Studies
Text categorization
Texts
sentential frequent itemsets
text classification
variable precision rough set model
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Summary:Text classification techniques mostly rely on single term analysis of the document data set, while more concepts, especially the specific ones, are usually conveyed by set of terms. To achieve more accurate text classifier, more informative feature including frequent co-occurring words in the same sentence and their weights are particularly important in such scenarios. In this paper, we propose a novel approach using sentential frequent itemset, a concept comes from association rule mining, for text classification, which views a sentence rather than a document as a transaction, and uses a variable precision rough set based method to evaluate each sentential frequent itemset’s contribution to the classification. Experiments over the Reuters and newsgroup corpus are carried out, which validate the practicability of the proposed system.
Bibliography:ObjectType-Article-2
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ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-007-9041-7