A two-level learning hierarchy of concept based keyword extraction for tag recommendations

CEUR Workshop Proceedings

Volumn 497, Issue , 2009, Pages 201-214

  Murfi, Hendri ^a   Obermayer, Klaus ^a  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

Author keywords

Concept extraction; Keyword extraction; Machine learning; Recommender system; Social tagging

Indexed keywords

BETTER PERFORMANCE; CONCEPT EXTRACTION; CONCEPT-BASED; KEYWORD EXTRACTION; SOCIAL TAGGING; SOCIAL TAGGING SYSTEMS; TAG RECOMMENDATIONS; TEXTUAL CONTENT;

EXPERIMENTS; EXTRACTION; LEARNING SYSTEMS; RECOMMENDER SYSTEMS; SOCIAL NETWORKING (ONLINE);

E-LEARNING;

EID: 78649307361     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. K. Barker and N. Cornacchia. Using noun phrase heads to extract document keyphrases. In Proceedings of the 13th Canadian Conference on Artificial Intelligence, pages 417-426, Montreal, Canada, 2000.
2. M. W. Berry, M. Browne, A. N. Langville, P. Pauca, and R. J. Plemmons. Algorithms and applications for approximate nonnegative matrix factorization. Computational Statistics and Data Analysis, 15(1):155-173, 2007.
3. W. M. Berry, S. T. Dumais, and G. W. O'Brien. Using linear algebra for intelligent information retrieval. SIAM Review, 37:573-595, 1995.
4. C. Boutsidis and E. Gallopoulos. Svd-based initialization: A head start on non-negative matrix factorization. Pattern Recognition, 41(4):1350-1362, 2008.
5. C. Carpineto and G. Romano. Using concept lattices for text retrieval and mining. In B. G. et al., editor, Formal Concept Analysis, pages 161-179. Springer-Verlag, 2005.
6. M. Chu, F. Diele, R. Plemmons, and S. Ragni. Optimality, computation, and interpretations of nonnegative matrix factorization. Technical report, Department of Mathematics, North Carolina State University, 2005.
7. S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. A. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6):391-407, 1990.
8. E. Frank, G. W. Paynter, I. H. Witten, C. Gutwin, and C. G. Nevill-Manning. Domain-specific keyphrase extraction. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 668-673, 1999.
9. S. Colder and B. Huberman. Usage patterns of collaborative tagging systems. Journal of Information Science, 32(2):198-208, 2006.
10. P. Heymann, D. Ramage, and H. G. Molina. Social tag prediction. In Proceeding of SIGIR, 2008.
11. A. Hotho, R. Jaschke, C. Schmitz, and G. Stumme. Information retrieval in folksonomies: Search and ranking. In Proceeding of the 3rd European Semantic Web Conference, pages 411-426, 2006.
12. P. Hoyer. Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 5:1457-1469, 1994.
13. A. Hulth. Combining Machine Learning and Natural Language Processing for Automatic Keyword Extraction. PhD thesis, Stockholm University, 2004.
14. R. Jaschke, L. Marinho, A. Hotho, L. S. Thieme, and G. Stumme. Tag recommendations in folksonomies. In J. N. Kok, J. Koronacki, R. L. de Ma'ntaras, S. Matwin, D. Mladenic, and A. Skowron, editors, The 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 506-514, 2007.
15. I. Katakis, G. Tsoumakas, and I. Vlahavas. Multilabel text classification for automated tag suggestion. In Proceeding of the ECML/PKDD Discovery Challenge, 2008.
16. F. W. Lancaster. Indexing and Abstracting in Theory and Practice. Library Association Publishing, 1998.
17. D. D. Lee and H. S. Seung. Learning the parts of objects by nonnegative matrix factorization. Nature, 401:788-791, 1999.
18. D. D. Lee and H. S. Seung. Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing System, pages 556-562, 2001.
19. C. J. Lin. On the convergence of multiplicative update algorithms for non-negative matrix factorization. IEEE Transactions on Neural Networks, 18:1589-1596, 2007.
20. C. J. Lin. Projected gradient methods for non-negative matrix factorization. Neural Computation, 19:2756-2779, 2007.
21. D. Lin and P. Pantel. Concept discovery from text. In Proceedings of the International Conference on Computational Linguistics, pages 577-583, 2002.
22. O. Medelyan and I. H. Witten. Domain-independent automatic keyphrase indexing with small training set. Journal of the American Society for Information Science and Technology, 59(7):1026-1040, 2008.
23. G. Mishne. Autotag: A collaborative approach to automated tag assignment for weblog posts. In Proceeding of the 15th International Conference on World Wide Web, pages 953-954, 2006.
24. P. Paatero and U. Tapper. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics, 5:111-126, 1994.
25. C. Paice and W. Black. A three-pronged approach to the extraction of key terms and semantic roles. In Proceedings of the International Conference on Recent Advances in NLP, pages 357-363, Borovets, Bulgaria, 2003.
26. U. Priss. Formal concept analysis in information science. Annual Review of Information Science and Technology, 40:521-543, 2005.
27. M. L. Reinberger and P. Spyns. Unsupervised text learning for the learning of dogma-inspired ontologies. In P. Buitelaar, P. Cimiano, and B. Magnini, editors, Ontology Learning from Text: Method, Application and Evaluation, pages 29-43. IOS Press, 2005.
28. S. C. Sood, S. H. Owsley, K. J. Hammond, and L. Birnbaum. Tagassist: Automatic tag suggestion for blog posts. In Proceeding of the International Conference on Weblogs and Social Media (ICWSM), 2007.
29. M. Tatu, M. Srikanth, and T. D'Silva. Rsdc'08: Tag recommendation using bookmark content. In Proceeding of the ECML/PKDD Discovery Challenge, 2008.
30. P. D. Turney. Learning algorithms for keyphrase extraction. Information Retrieval, 2(4):303-336, 2000.
31. Z. Xu, Y. Fu, J. Mao, and D. Su. Towards the semantic web: Collaborative tag suggestions. In Proceeding of Collaborative Web Tagging Workshop, 2006.