Enriching short text representation in microblog for clustering

Frontiers of Computer Science in China

Volumn 6, Issue 1, 2012, Pages 88-101

  Tang, Jiliang ^a   Wang, Xufei ^a   Gao, Huiji ^a   Hu, Xia ^a   Liu, Huan ^a  

^a Arizona State University   (United States)

Author keywords

matrix factorization; multi language knowledge; short texts; social media; text representation

Indexed keywords

MATRIX FACTORIZATIONS; MULTI-LANGUAGE KNOWLEDGE; SHORT TEXTS; SOCIAL MEDIA; TEXT REPRESENTATION;

FACTORIZATION; INFORMATION DISSEMINATION; MATRIX ALGEBRA; SOCIAL NETWORKING (ONLINE);

TEXT PROCESSING;

EID: 84863014790     PISSN: 16737350     EISSN: 16737466     Source Type: Journal    
DOI: 10.1007/s11704-011-1167-7     Document Type: Article

References (22)

1. Adamic L A, Zhang J, Bakshy E, Ackerman M S. Knowledge sharing and yahoo answers: everyone knows something. In: Proceedings of 17th International Conference on World Wide Web. 2008, 665-674.
2. Hotho A, Staab S, Stumme G. Wordnet improves text document clustering. In: Proceedings of 2003 SIGIR Semantic WebWorkshop. 2003, 541-544.
3. Reforgiato Recupero D. A new unsupervised method for document clustering by using WordNet lexical and conceptual relations. Information Retrieval, 2007, 10(6): 563-579.
4. Hu J, Fang L, Cao Y, Zeng H J, Li H, Yang Q, Chen Z. Enhancing text clustering by leveraging Wikipedia semantics. In: Proceedings of 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2008, 179-186.
5. Hu X, Zhang X, Lu C, Park E K, Zhou X. Exploiting Wikipedia as external knowledge for document clustering. In: Proceedings of 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009, 389-396.
6. Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993-1022.
7. Hofmann T. Probabilistic latent semantic indexing. In: Proceedings of 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 1999, 50-57.
8. Xu W, Liu X, Gong Y. Document clustering based on non-negative matrix factorization. In: Proceedings of 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2003, 267-273.
9. Lin C J. Projected gradient methods for non-negative matrix factorization. Neural Computation, 2007, 19(10): 2756-2779.
10. Cutting D R, Pedersen J O, Karger D R, Tukey J W. Scatter/gather: a cluster-based approach to browsing large document collections. In: Proceedings of 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 1992, 318-329.
11. Dave K, Lawrence S, Pennock D M. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of 12th International Conference on World Wide Web. 2003, 519-528.
12. Steinbach M, Karypis G, Kumar V. A comparison of document clustering techniques. In: Proceedings of 2000 KDD Workshop on Text Mining. 2000, 525-526.
13. Banerjee S, Ramanathan K, Gupta A. Clustering short texts using Wikipedia. In: Proceedings of 30th Annual International ACM SIGIR Conference on Research and Development. 2007, 787-788.
14. Lee D D, Seung H S. Algorithms for non-negative matrix factorization. In: Proceedings of 2000 Neural Information Processing Systems. 2000, 556-562.
15. Hu X, Sun N, Zhang C, Chua T S. Exploiting internal and external semantics for the clustering of short texts using world knowledge. In: Proceedings of 18th ACM Conference on Information and Knowledge Management. 2009, 919-928.
16. Halkdi M, Nguyen B, Varlamis I, Vazirgiannis M. THESUS: organizing Web document collections based on link sematics. The VLDB Journal, 2003, 12(4): 320-332.
17. Yoo I, Hu X, Song I Y. Integration of semantic-based bipartite graph representation and mutual refinement strategy for biomedical literature clustering. In: Proceedings of 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006, 791-796.
18. Gabrilovich E, Markovitch S. Feature generation for text categorization using world knowledge. In: Proceedings of 19th International Joint Conference on Artificial Intelligence. 2005, 1048-1053.
19. Gabrilovich E, Markovitch S. Overcoming the brittleness bottleneck using Wikipedia: enhancing text categorization with encyclopedic knowledge. In: Proceedings of 21st National Conference on Artificial Intelligence, Vol 2. 2006, 1301-1306.
20. Fodeh S, Punch B, Tan P N. On ontology-driven document clustering using core semantic features. Knowledge and Information Systems, 2011, 28(2): 395-421.
21. Kasneci G, Ramanath M, Suchanek F, Weikum G. The YAGO-NAGA approach to knowledge discovery. ACM SIGMOD Record, 2008, 37(4): 41-47.
22. Theobald M, Bast H, Majumdar D, Schenkel R, Weikum G. TopX: efficient and versatile top-k query processing for semistructured data. The VLDB Journal, 2008, 17(1): 81-115.