Review on Semantic Text Categorization

Article Preview

Abstract:

Text classification has been a hot research in recent years. This text reviewed the history of text classification. It summarized some common classification methods and mainly introduced classification methods based on semantic. Especially, it elaborated the text classification based on ontology, the text classification based on similarity computation and the text classification based on latent semantic indexing.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2323-2328

Citation:

Online since:

September 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Sebastiani F. Machine learning in automated text categorization, ACM Computing Surveys. 2002, 34 (1), 1−4.

DOI: 10.1145/505282.505283

Google Scholar

[2] Fang Yu, Yunfei Jiang A feature selection method based on naive Bias classification[J]. Journal of Sun Yat-sen(Natural Science Edition), 2004, 43(5).

Google Scholar

[3] Kazama J, Tsujii J. Maximum entropy models with inequality constraints: A case study on text categorization, Machine Learning. 2005, 60(1-3), 159−194.

DOI: 10.1007/s10994-005-0911-3

Google Scholar

[4] Li R, Wang J, Chen X, Tao X, Hu Y. Using maximum entropy model for Chinese text categorization, Journal of Computer.

Google Scholar

[5] Yang Y M, Liu X. A re-examination of text categorization methods [C]. Proceedings 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR'99), Berkeley: ACM Press, 1999, 42-49.

DOI: 10.1145/312624.312647

Google Scholar

[6] Miaomiao Tian. Research on text classification based on decision tree [J]. Jilin Normal University(Natural Science Edition), 2008, 1, 54-56.

Google Scholar

[7] Jingrui Shi, Yuming Zheng, Xi Han. Application of artificial neural network in text categorization [J]. Application research of computers, 2005, 10, 213-216.

Google Scholar

[8] Xiaoyan Zhang, Qiang Li. Review of classification method based on SVM[J]. Science & technology information, 2008, 28, 12-13.

Google Scholar

[9] Lei Wang . Research on improved text classifier based on Bayesian algorithm and Its application in the NERIIS [D]Journal of University, (2006).

Google Scholar

[10] Cover T M, H art P E. Nearest neighbor pattern classification [J]. IEEE Transactions on Information Theory, 1967, 13(1), 21 -27.

DOI: 10.1109/tit.1967.1053964

Google Scholar

[11] Qiang Qian, Linbin Pang , Shang Gao . A text categorization algorithm based on improved K N N algorithm [J]. college journal of Jiangsu University of Science and Technology(Natural Science Edition), 2013, 27(4).

Google Scholar

[12] Chakrabarti S, Roy S, Soundalgekar M. Fast and accurate text classification via multiple linear discriminant projections, Int'l Journal on Very Large Data Bases. 2003, 12(2), 170−185.

DOI: 10.1007/s00778-003-0098-9

Google Scholar

[13] Wu H, Phang TH, Liu B, Li X. A refinement approach to handling model misfit in text categorization. In: Davis H, Daniel K, Raymoind N, eds. Proc. of the 8th ACM Int'l Conf. on Knowledge Discovery and Data Mining (SIGKDD-02). Edmonton: ACM.. Press, 2002, 207−216.

DOI: 10.1145/775047.775078

Google Scholar

[14] Wang J, Wang H, Zhang S, Hu Y. A simple and efficient algorithm to classify a large scale of text, Journal of Computer Research and Development. 2005, 42(1), 85−93 (in Chinese with English abstract).

Google Scholar

[15] Tan S, Cheng X, Wang B, Xu H, Ghanem MM, Guo Y. Using dragpushing to refine centroid text classifiers. In: Ricardo ABY, Nivio Z, Gary M, Alistair M, John T, eds. Proc. of the ACM SIGIR-05. Salvador: ACM Press, 2005, 653-654.

DOI: 10.1145/1076034.1076174

Google Scholar

[16] Debole F, Sebastiani F. An analysis of the relative hardness of reuters-21578 subsets, Journal of the American Society for Information Science and Technology. 2004, 56(6), 584−596.

DOI: 10.1002/asi.20147

Google Scholar

[17] Chen Enhong, Wu Gaofeng. An Ontology Learning Method Enhancedby Frame Semantics [J]. Proceedings of the Seventh IEEE International Symposiumon Multimedia, 2005 374382.

DOI: 10.1109/ism.2005.32

Google Scholar

[18] Xiantang Huang . Research on semantic Web text classification based on Ontology [J]. Library, 2009, 3(3), 47-49.

Google Scholar

[19] Dongwen Lin, Qingyuan Bai, Licong Xie, Huosheng Xie , Ying Zhang . A method of selecting the text features based on Ontology [J]. Computer science, 2009, (3), 142-145.

Google Scholar

[20] Yahui Ning , Xinghua Fan, Yu Wu. Short text classification based on domain word ontology [J]. Computer science, 2009, 36(3), 142-145.

Google Scholar

[21] Ying Zhang , Wenjie Wang, Zhongzhi Shi . Text classification method based on Ontology [J]. Computer simulation, 2009, 26(5), 103-106.

Google Scholar

[22] Tingting Wei, Dengguo Nie , Ju Wang, Yuncheng Jiang. Text classification method based on domain ontologies [J], Computer Engineering. 2012, 38(15), 62-65.

Google Scholar

[23] Ling Li . Enterprise oriented process diagnosis knowledge similarity matching research and development tools [D]. Harbin. Harbin Institute of Technology, (2006).

Google Scholar

[24] Shenyan Chen , Junhua Wu . Concept semantic similarity computation based on ontology and its application [J]. Microelectronics and computer, 2008, 15(12), 96-99.

Google Scholar

[25] Yupeng Zong , Gang Wu. A Semantic similarity algorithm based on contextual[J], Microcomputer information, 2008, 24(10-3), 211-212.

Google Scholar

[26] Deerwester S, et al. Harshman R. Indexing by Latent Semantic Analysis [J]. Journal of the American Society of Information Science, 1990, 41 (6), 391-407.

DOI: 10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9

Google Scholar

[27] Dumais S T. Using LSI for Information Filtering [M]. The Third Text Retrieval ConferenceTREC-3), D. Harman, eds, National Institute of Standards and Technology Special Publication, (1995).

Google Scholar

[28] Landauer T K, Dumais S T. A Solution to Plato's Problem: the Latent Semantic Analysis Theory of the Acguisition, Induction and Representation of Knowledge [J]. Psychological Review, 1997, 104, 211-240.

DOI: 10.1037/0033-295x.104.2.211

Google Scholar

[29] Xueqiang Zeng and so on. A text categorization modelbased on latent semantic structure [J] (searching engine and Web Mining Conference in 2004). South China University of Technology ( Natural Science Edition), 2004, 32, 99-102.

Google Scholar

[30] M, Nie J . A Latent Semantic Structure Model for Text Classification [M]. ACM-SIGIR-2003, Workshop on Mathematic/Formal Methods in Information Retrieval, Toronto, Canada, (2003).

Google Scholar

[31] Hao Ye, Mingwen Wang , Xueqiang Zeng. Research on multi class text classification model based on Latent Semantic [J]. Journal of Tsinghua University(Natural Science Edition), 2005, 45(S1), 1818-1822.

Google Scholar