Learning to classify short and sparse text & web with hidden topics from large-scale data collections

Proceeding of the 17th International Conference on World Wide Web 2008, WWW'08

Volumn , Issue , 2008, Pages 91-99

  Phan, Xuan Hieu ^a   Nguyen, Le Minh ^b   Horiguchi, Susumu ^a  

^a TOHOKU UNIVERSITY   (Japan)

^b JAPAN ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

Sparse text; Topic analysis; Web data analysis classification

Indexed keywords

CLASSIFIERS; DATA ACQUISITION; INFORMATION RETRIEVAL; INTERNET; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; TEXT PROCESSING;

CLASSIFICATION TASKS; DATA COLLECTIONS; DATA DOMAINS; DATA SPARSENESSES; DO-MAINS; EXTERNAL KNOWLEDGES; EXTERNAL-; GENERAL FRAMEWORKS; HIGH ACCURACIES; LABELED TRAINING DATUMS; MEDICAL TEXTS; MEDLINE; NEWS FEEDS; QUALITY ENHANCEMENTS; SHORT SEGMENTS; SPARSE TEXT; TOPIC ANALYSIS; WEB DATA ANALYSIS/CLASSIFICATION; WEB SEARCHES; WIKIPEDIA;

WORLD WIDE WEB;

EID: 57349117605     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1367497.1367510     Document Type: Conference Paper

References (36)

1. C. Andrieu, N. Freitas, A. Doucet, and M. Jordan. An introduction to MCMC for machine learning. Machine Learning, 50:5-43, 2003.
2. L. Baker and A. McCallum. Distributional clustering of words for text classification. Proc. ACM SIGIR, 1998.
3. P. Baldi, P. Frasconi, and P. Smyth. Modeling the Internet & the Web: probabilistic methods & algorithms. Wiley, 2003.
4. S. Banerjee, K. Ramanathan, and A. Gupta. Clustering short texts using Wikipedia. Proc. ACM SIGIR, 2007.
5. A. Berger, A. Pietra, and J. Pietra. A maximum entropy approach to natural language processing. Computational Linguistics, 22(1):39-71, 1996.
6. R. Bekkerman, R. El-Yaniv, N. Tishby, and Y. Winter. Distributional word clusters vs. words for text categorization. JMLR, 3:1183-1208, 2003.
7. I. Bhattacharya and L. Getoor. A latent Dirichlet model for unsupervised entity resolution. Proc. SIAM SDM, 2006.
8. D. Blei, A. Ng, and M. Jordan. Latent Dirichlet Allocation. JMLR, 3:993-1022, 2003.
9. D. Blei and J. Lafferty. A correlated topic model of Science. The Annals of Applied Statistics, 1(1):17-35, 2007.
10. D. Bollegala, Y. Matsuo, and M. Ishizuka. Measuring semantic similarity between words using Web search engines. Proc. WWW, 2007.
11. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. Proc. COLT, 1998.
12. L. Cai and T. Hofmann. Text categorization by boosting automatically extracted concepts. Proc. ACM SIGIR, 2003.
13. 13 J. Cai, W. Lee, and Y. Teh. Improving WSD using topic features. Proc. EMNLP-CoNLL, 2007.
14. S. Deerwester, G. Furnas, and T. Landauer. Indexing by latent semantic analysis. Journal of the American Society for Info. Science, 41(6):391-407, 1990.
15. L. Denoyer and P. Gallinari. The Wikipedia XML Corpus. ACM SIGIR Forum, 2006.
16. I. Dhillon and D. Modha. Concept decompositions for large sparse text data using clustering. Machine Learning, 29 (2-3): 103-130, 2001.
17. E. Gabrilovich and S. Markovitch. Computing semantic relatedness using Wikipedia-based explicit semantic analysis. Proc. IJCAI, 2007.
18. S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE PAMI, 6:721-741, 1984.
19. T. Griffiths and M. Steyvers. Finding scientific topics. The National Academy of Sciences, 101:5228-5235, 2004.
20. T. Joachims. Text categorization with SVMs: learning with many relevant features. Proc. ECML, 1998.
21. G. Heinrich. Parameter estimation for text analysis. Technical report, 2005.
22. T. Hofmann. Probabilistic LSA. Proc. UAI, 1999.
23. T. Hofmann. Latent semantic models for collaborative filtering. ACM TOIS, 22(1):89-115, 2004.
24. F. Keller, M. Lapata, and O. Ourioupina. Using the Web to overcome data sparseness. Proc. EMNLP, 2002.
25. K. Kummamuru, R. Lotlikar, S. Roy, K. Singal, and R. Krishnapuram. A hierarchical monothetic document clustering algorithm for summarization and browsing search results. Proc. WWW, 2004.
26. D. Liu and J. Nocedal. On the limited memory BFGS method for large-scale optimization. Mathematical Programming, 45:503-528, 1989.
27. D. Metzler, S. Dumais, and C. Meek. Similarity measures for short segments of text. Proc. ECIR, 2007.
28. T. Minka and J. Lafferty. Expectation-propagation for the generative aspect model. Proc. UAI, 2002.
29. K. Nigram, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2-3): 103-134, 2000.
30. M. Sahami and T. Heilman. A Webnbased kernel function for measuring the similarity of short text snippets. Proc. WWW, 2006.
31. P. Schonhofen. Identifying document topics using the Wikipedia category network. Proc. the IEEE/WIC/ACM International Conference on Web Intelligence, 2006.
32. F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1-47, 2002.
33. X. Wei and W. Croft. LDA-based document models for ad-hoc retrieval. Proc. ACM SIGIR, 2006.
34. W. Yih and C. Meek. Improving similarity measures for short segments of text. Proc. AAAI, 2007.
35. O. Zamir and O. Etzioni. Grouper: a dynamic clustering interface to Web search results. Proc. WWW, 1999.
36. H. Zeng, Q. He, Z. Chen, W. Ma, and J. Ma. Learning to cluster Web search results. Proc. ACM SIGIR, 2004.