Abstract
Traditional approaches in document categorization use the term-based classification techniques to classify the documents. The techniques, for enormous terms, are not effective to the applications that need speedy response or not much space. This paper presents an effective concept-based document categorization system, which can efficiently classify Korean documents through the thesaurus tool. The thesaurus tool is the information extractor that acquires the meanings of document terms from the thesaurus. It supports effective document categorization with the acquired meanings. The system uses the concept-probability vector to represent the meanings of the terms. Because the category of the document depends on the meanings than the terms, even though the size of the vector is small, the system can classify the document without degradation of the performance. The system uses the small concept-probability vector so that it can save the time and space for document categorization. The experimental results suggest that the presented system with the thesaurus tool can effectively classify the documents. The results show that even though the system uses the contracted vector for document categorization, the performance of the system is not degraded.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Linoff, M.D., Waltz, D.: Classifying News Stories using Memory Based Reasoning. In: Proc. Intl. Conf. on Research and Development in Information Retrieval, ACM SIGIR, pp. 59–65 (1992)
Wong, K.M., Yao, Y.Y.: A Statistical Similarity Measure. In: Proc. Intl. Conf. on Research and Development in Information Retrieval, ACM SIGIR, pp. 3–12 (1987)
Kweon, O.W.: Optimizing for Text Categorization Using Probability Vector and Meta Category, M.S. Thesis, KAIST Computer Science Dept. (1995)
Hayes, J.: Intelligent High-Volume Text Processing Using Shallow, Domain-Specific Technique. In: Jacobs, P.S. (ed.) Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval, Hillsdale, New Jersey, pp. 227–241 (1992)
Yang, Y.: Expert Network: Effective and Efficient Learning from Human Decision in Text Categorization and Retrieval. In: Proc. Intl. Conf. on Research and Development in Information Retrieval, ACM SIGIR, pp. 13–22 (1994)
ETRI Natural Language Processing Lab.: ETRIKEMONG SET, ETRI (1997)
Lee, H.A., Lee, J.H., Lee, G.B.: Concept-based Noun Phrase Indexing Method Using Syntactic Analysis and Cooccurence Information. In: Proc. of the 7th Hanguel and Korean Information Processing Conference (1996)
EDR Technical Report: Concept Dictionary, Japan Electronic Dictionary Research Institute (1988)
Kang, W.S.: Semantic Analysis of Prepositional Phrases in English-to-Korean Machine Translation, KAIST Ph.D. Thesis (1995)
Kim, S.Y.: Morphological Analyzer using Tabular Parsing Method and Concatenation Information, KAIST M.S. Thesis (1987)
Lewis, D.D.: An Evaluation of Phrasal and Clustered Representations on a Text Categorization Task. In: ACM SIGIR 1992 (1992)
Apte, C., Famerau, F., Weiss, S.M.: Automated Learning of Decision Rules for Text Categorization. ACM Tr. on Information Systems 12(3) (1994)
Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.: Introduction to Word-Net: An On-line Lexical Database, Report of WordNet, Princeton University (1990)
Sebstiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Yang, Y., Zhang, J., Kisiel, B.: A Scalability Analysis of Classifiers in Text Categorization. In: Proceedings of SIGIR 2003, 26th ACM International Conference, pp. 96–103 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kang, HK. et al. (2005). CONDOCS: A Concept-Based Document Categorization System Using Concept-Probability Vector with Thesaurus. In: Kim, T.G. (eds) Artificial Intelligence and Simulation. AIS 2004. Lecture Notes in Computer Science(), vol 3397. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30583-5_72
Download citation
DOI: https://doi.org/10.1007/978-3-540-30583-5_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24476-9
Online ISBN: 978-3-540-30583-5
eBook Packages: Computer ScienceComputer Science (R0)