Exploiting semantic resources for large scale text categorization

Journal of Intelligent Information Systems

Volumn 39, Issue 3, 2012, Pages 763-788

  Li, Jian Qiang ^a   Zhao, Yu ^a   Liu, Bo ^a  

^a NEC Laboratories China   (China)

Author keywords

Semantic analysis; Semantic information processing; Web scale text categorization

Indexed keywords

AUTOMATIC CATEGORIZATION; AUTOMATIC TRAINING; DATA LABELING; DISCRIMINATIVE CLASSIFIERS; DOCUMENT CLUSTERING; EMPIRICAL EXPERIMENTS; LABELED DATA; LABOR INTENSIVE; LEXICAL DATABASE; SEMANTIC ANALYSIS; SEMANTIC INFORMATION; SEMANTIC RESOURCES; SEMI-SUPERVISED METHOD; STATISTIC ANALYSIS; SUPERVISED CLASSIFIERS; TEXT CATEGORIZATION; TRAINING DATA; UNLABELED DOCUMENTS;

CLASSIFICATION (OF INFORMATION); DATA PROCESSING; SEMANTIC WEB; SEMANTICS; TEXT PROCESSING;

CLASSIFIERS;

EID: 84869499954     PISSN: 09259902     EISSN: 15737675     Source Type: Journal    
DOI: 10.1007/s10844-012-0211-x     Document Type: Article

References (51)

1. Alan Smeaton, F. (1999). Using NLP or NLP resources for information retrieval tasks. In Natural language information retrieval. Dordrecht, NL: Kluwer Academic Publishers.
2. Bai, R., Wang, X., & Liao, J. (2010). Extract semantic information from WordNet to improve text classification performance. In Proceedings of the international conference on Advances in computer science and information technology, June 23-25, 2010, LNCS 6059 (pp. 409-420).
3. Banerjee, S., & Pedersen, T. (2002). An adapted lesk algorithm for word sense disambiguation using WordNet. In Proceedings of the Third International Conference on Intelligent Text Processing and Computational Linguistics (pp. 136-145).
4. Basili, R., Cammisa, M., & Moschitti, A. (2005). Effective use of Wordnet semantics via kernelbased learning. In Proceedings of the 9th conference on computational natural language learning (CoNLL 2005). USA, Ann Arbor (MI).
5. Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In Proc. of the workshop on computational learning theory (pp. 92-100).
6. Bradford, R. (2008). An empirical study of required dimensionality for large-scale latent semantic indexing applications. In Proceedings of the 17th ACM conference on information and knowledge management (pp. 153-162). California, USA: Napa Valley. CoreNet (2012). http://korterm.kaist.ac.kr.
7. de Buenaga Rodriguez, M., Gomez-Hidalgo, J., & Diaz-Agudo, B. (1997). Using WordNet to complement training information in text categorization. In Proceedings of the 2nd International Conference on Recent Advances in Natural Language Processing (RANLP'97) (pp. 150-157).
8. Deerwester, S., Dumais, S., Furnas, G., Landauer, T., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391-407.
9. Ellen Voorhees, M. (1993). Using wordnet to disambiguate word senses for text retrieval. In Proceedings SIGIR'93. PA, USA: Pittsburgh.
10. EuroWordNet (2012). http://www.illc.uva.nl/EuroWordNet.
11. Ferrández, S., Toral, A., Ferrández, O., Ferrández, A.,&Muñoz, R. (2009). Exploiting wikipedia and EuroWordNet to solve cross-lingual question answering. Information Sciences, 179(20), 3473-3488.
12. Gabrilovich, E., & Markovitch, S. (2005). Feature generation for text categorization using world knowledge. In International joint conference on artificial intelligence. Scotland: Edinburgh.
13. Gabrilovich, E., & Markovitch, S. (2006). Overcoming the brittleness bottleneck using wikipedia: Enhancing text categorization with encyclopedic knowledge. In National conference on artificial intelligence (AAAI). Massachusetts: Boston.
14. Gliozzo, A. M., & Strapparava, C. (2005). Domain kernels for text categorization. In Proceedings of the ninth conference on computational natural language learning (CoNLL-2005) (pp. 56-63). Michigan: Ann Arbor.
15. Gliozzo, A. M., Strapparava, C., & Dagan, I. (2005). Investigating unsupervised learning for text categorization bootstrapping. In Proceedings of the joint Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP) (pp. 129-136).
16. Hotho, A., Staab, S., & Stumme, G. (2003). Wordnet improves text document clustering. In Proc. of the semantic web workshop at SIGIR (pp. 541-544).
17. Hownet (2012). http://www.keenage.com
18. Ide, N., & Véronis, J. (1998). Word sense disambiguation: the state of the art. Computational Linguistics, 24(1), 1-40.
19. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In European conference on machine learning.
20. Joachims, T. (1999). Transductive inference for text classification using support vector machines. In Proc. 16th international conf. on machine learning (pp. 200-209).
21. Kehagias, A., Petridis, V., Kaburlasos, V., & Fragkou, P. (2003). A comparison of word-and sensebased text classification using several classification algorithms. Journal of Intelligent Information Systems, 21(3), 227-247.
22. Ko, Y., & Seo, J. (2000). Automatic text categorization by unsupervised learning. In Proceedings of the 18th International Conference on Computational Linguistics (COLING) (pp. 453-459).
23. Li, J. Q., Zhao, Y., & Liu, B. (2009). Fully automatic text categorization by exploiting WordNet. In Proceeding of Asia information retrieval societies conference, LNCS 5839 (pp. 1-12). Springer:New York/Heidelberg.
24. Li, C.H., Yang, J. C.,&Park, S. C. (2012). Text categorization algorithms using semantic approaches, corpus-based thesaurus and WordNet. Expert Systems with Applications, 39(1), 765-772.
25. Liu, B., Li X., Lee, W. S., & Yu, P. S. (2004). Text classification by labeling words. In Proc. 19th nat'l conf. artificial intelligence (pp. 425-430).
26. Liu, T., Yang, Y., Wan, H., Zhou, Q., Gao, B., Zeng, H. J., et al. (2005). An experimental study on large-scale web categorization. In Posters Proceedings of the 14th International World Wide Web Conference (pp. 1106-1107).
27. Luo, Q., Chen, E., & Xiong, H. (2011). A semantic term weighting scheme for text categorization. Expert Systems with Applications, 38(10), 12708-12716.
28. Mansuy, T. N., & Hilderman, R. J. (2006). A characterization of wordnet features in boolean models for text classification. In AusDM (pp. 103-109).
29. McCallum, A., & Nigam, K. (1998). A comparison of event models for naive bayes text classification. In AAAI/ICML-98 workshop on learning for text categorization (pp. 41-48).
30. Mohammed, M. & Mohammed, B. (2011). On the merging of domain-specific heterogeneous ontologies using WordNet and web pattern-based queries. Journal of Information and Knowledge Management, 10(1), 23-36.
31. Moldovan, D. I., & Mihalcea, R. (2000). Using WordNet and lexical operators to improve internet searches. IEEE Internet Computing, 4(1), 34-43.
32. Navigli, R., Faralli, S., Soroa, A., Lacalle, O. L., & Agirre, E. (2011). Two birds with one stone: Learning semantic models for text categorization and word sense disambiguation. In Proc. of the 20th ACM Conference on Information and Knowledge Management (CIKM 2011), Glasgow, UK, October 24-28th (pp. 2317-2320).
33. Nigam, K., Lafferty, J., & McCallum, A. (1991). Using maximum entropy for text classification. In IJCAI-99 workshop on machine learning for information filtering (pp. 61-67).
34. Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2/3), 103-134.
35. Peng, X., & Choi, B. (2005). Document classifications based on word semantic hierarchies. In Proc. of the international conf. on artificial intelligence and application (AIA'05) (pp. 362-367).
36. Salon, G. (1991). Development in automatic text retrieval. Science, 253, 974-979.
37. Scott, S., &Matwin, S. (1998). Text classification using wordNet hypernyms. In Proc. Coling-ACL'98 (pp. 45-52).
38. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1-47.
39. Silverman, B.W. (1986). Density estimation for statistics and data analysis. New York: Chapman and Hall.
40. Siolas, G., & d'Alch Buc, F. (2000). Support vector machines based on a semantic kernel for text categorization. In Proceedings of the IEEE-INNS-ENNS international joint conference on neural networks (IJCNN'00) (Vol. 5, p. 5205). IEEE Computer Society: Washington, DC.
41. Sogou Labs (2012). http://www.sogou.com/labs/resources.html.
42. SVM-light (2012). http://svmlight.joachims.org/.
43. Vapnik, V. (1995). The nature of statistical learning theory. NY, USA: Springer-Verlag.
44. Wang, P., & Domeniconi, C. (2008). Building semantic kernels for text classification using wikipedia. In The 14th ACM SIGKDD (pp. 713-721). New York: ACM Press.
45. Weka (2012). http://www.cs.waikato.ac.nz/ml/weka/.
46. Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd Edn.). San Francisco: Morgan Kaufmann.
47. WordNet (2012). http://wordnet.princeton.edu/.
48. Yang, Y.,&Liu, X. (1999).Are-examination of text categorization methods. In Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'99) (pp. 42-49).
49. Zeng, H. J., et al. (2003). CBC: Clustering based text classification requiring minimal labeled data. In ICDM (pp. 443-450).
50. Zhang Y., Gong, L. & Wang, Y. (2005). Chinese word sense disambiguation using HowNet. Lecture Notes in Computer Science, 3610/2005, 925-932.
51. Zhu, X. J. (2007). Semi-supervised learning literature survey. http://pages.cs.wisc.edu/-jerryzhu/research/ssl/semireview.html.