Integrating background knowledge into nearest-neighbor text classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2416, Issue , 2002, Pages 1-5

  Zelikovitz, Sarah ^a   Hirsh, Haym ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CASE BASED REASONING; CLASSIFICATION (OF INFORMATION); SEMANTICS; WORLD WIDE WEB;

BACK-GROUND KNOWLEDGE; DOCUMENT SIMILARITY; LABOR INTENSIVE; LARGE AMOUNTS; LATENT SEMANTIC INDEXING; NEAREST NEIGHBORS; SPECIFIC PROBLEMS; TEXT CLASSIFICATION;

TEXT PROCESSING;

EID: 84942780408     PISSN: 03029743     EISSN: 16113349     Source Type: Journal    
DOI: 10.1007/3-540-46119-1_1     Document Type: Article

References (6)

1. William Cohen. A web-based information system that reasons with structured collections of text. Proceedings of Autonomous Agents, 1998.
2. W. Cohen and H. Hirsh. Joins that generalize: Text categorization using WHIRL. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pages 169-173, 1998.
3. S. Deerwester, S. Dumais, G. Furnas, and T. Landauer. Indexing by latent semantic analysis. Journal for the American Society for Information Science, 41(6):391-407, 1990.
4. S. Zelikovitz. Using Background Knowledge to Improve Text Classification. PhD thesis, Rutgers University, 2002.
5. S. Zelikovitz and H. Hirsh. Improving short text classification using unlabeled background knowledge to assess document similarity. Proceedings of the Seventeenth International Conference on Machine Learning, pages 1183-1190, 2000.
6. S. Zelikovitz and H. Hirsh. Using LSI for text classification in the presence of background text. Proceedings of the Tenth Conference for Information and Knowledge Management, 2001.