Subject metadata enrichment using statistical topic models

Proceedings of the ACM International Conference on Digital Libraries

Volumn , Issue , 2007, Pages 366-375

  Newman, David ^a   Hagedorn, Kat ^b   Chemudugunta, Chaitanya ^a   Smyth, Padhraic ^a  

^a Irvine   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Browsing; Clustering; Digital libraries; Metadata enhancement; Metadata enrichment; OAI; Topic model

Indexed keywords

METADATA ENHANCEMENT; METADATA ENRICHMENT; TOPIC MODELS;

CLUSTERING ALGORITHMS; COMPUTER SIMULATION; INFORMATION RETRIEVAL; METADATA;

DIGITAL LIBRARIES;

EID: 34548202975     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1255175.1255248     Document Type: Conference Paper

References (14)

1. Blei, D., Ng, A., Jordan, M. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
2. Buntine, W., Lofstrom, J., Perki, J., Perttu, S., Poroshin, V., Silander, A Scalable Topic-Based Open Source Search Engine. In IEEE/WIC/ ACMInternational Conference on Web Intelligence, 228-234, 2004.
3. Chemudugunta, C., Smyth, P., Steyvers, M., Modeling general and specific aspects of documents with a probabilistic topic model. In NIPS'06, Advances in Neural Information Processing Systems 19,. 2006.
4. Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., Harshman, R A. Indexing by latent semantic analysis. JASIS, 41(6):391-407, 1990.
5. Dhillon, I.S, Modha, D.S., Concept decompositions for large sparse text data using clustering. Machine Learning. 42:143-175, 2001.
6. Griffiths, T., Steyvers, M., Finding Scientific Topics. PNAS, 101(suppl. 1):5228-5235. 2004.
7. Hoffman, T. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42, 177-196, 2001.
8. Krowne, A., Halbert, M. An initial evaluation of automated organization for digital library browsing. Joint Conference on Digital Libraries. pp246-255. June 7-11, 2005
9. Lee, D., Seung, H. S., Learning the parts of objects by nonnegative matrix factorization. Nature, v.401, 788-791, 1999.
10. Li, W., McCallum, A. Pachinko Allocation: DAG-structured Mixture Models of Topic Correlations. In ICML'06. 2006
11. Mann, G. S., Mimno, D., McCallum, A. Bibliometric impact measures leveraging topic analysis. Joint Conference on Digital Libraries. pp65-74. June 11-15,. 2006.
12. Manning, C., Schutze, H. Foundations of Statistical Natural Language Processing, MIT Press. Cambridge, MA: 1999.
13. Newman, D., Block, S. Probabilistic Topic Decomposition of and Eighteenth Century Newspaper. JASIST, 57(6):753-767, 2006.
14. Newman, D., Chemudugunta, C., Smyth, P., Steyvers, M. Analyzing Entities and Topics in News Articles Using Statistical Topic Models. In LNCS - IEEE Conference on Intelligence and Security Informatics. pp93-104. San Diego, 2006.