Genre classification of web pages: User study and feasibility analysis

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

Volumn 3238 LNAI, Issue , 2004, Pages 256-269

  Meyer Zu Eissen, Sven ^a   Stein, Benno ^a  

^a UNIVERSITY OF PADERBORN   (Germany)

Author keywords

Genre classification; Information need; Information retrieval; Machine learning; User study; WWW

Indexed keywords

ARTIFICIAL INTELLIGENCE; DISCRIMINANT ANALYSIS; INFORMATION RETRIEVAL; LEARNING SYSTEMS; SUPPORT VECTOR MACHINES; WEBSITES; WORLD WIDE WEB; INFORMATION RETRIEVAL SYSTEMS; INFORMATION SCIENCE;

COMPACT FEATURES; COMPUTATIONAL EFFORT; FEASIBILITY ANALYSIS; GENRE CLASSIFICATION; INFORMATION NEED; NEURAL NETWORK LEARNING; USER STUDY; WORD FREQUENCIES;

INFORMATION RETRIEVAL SYSTEMS; CLASSIFICATION (OF INFORMATION);

EID: 77049104346     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30221-6_20     Document Type: Conference Paper

References (16)

1. D. Biber. The multidimensional approach to linguistic analyses of genre variation: An overview of methodology and findings. In Computers and the Humanities, volume 26, pages 331-345, 1992.
2. I. Bretan, J. Dewe, A. Hallberg, and N. Wolkert. Web-specific genre visualization, 1999.
3. K. Crowston and M. Williams. The effects of linking on genres of web documents. In HICSS, 1999.
4. S. Dennis. The sydney morning herald word database. http://www2.psy.uq. edu.au/CogPsych/Noetica/OpenForumIssue4/SMH.html, 1995.
5. N. Dewdney, C. VanEss-Dykema, and R. MacMillan. The form is the substance: Classifi-cation of genres in text. In Proceedings of ACL Workshop on HumanLanguage Technology and Knowledge Management, 2001.
6. M. Dimitrova, A. Finn, N. Kushmerick, and B. Smyth. Web genre visualization. In Proceedings of the Conference on Human Factors in Computing Systems, 2002.
7. A. Finn and N. Kushmerick. Learning to classify documents according to genre. In IJCAI-03 WS on Computational Approaches to Style Analysis and Synthesis, 2003.
8. J. Karlgren and D. Cutting. Recognizing text genres with simple metrics using discriminant analysis. In Proceedings of the 15th. International Conference on Computational Linguistics (COLING 94), volume II, pages 1071 - 1075, Kyoto, Japan, 1994.
9. B. Kessler, G. Nunberg, and H. Schütze. Automatic detection of text genre. In P. R. Cohen and W. Wahlster, editors, Proceedings of the Thirty-Fifth Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, pages 32-38, Somerset, New Jersey, 1997. Association for Computational Linguistics.
10. Y.-B. Lee and S. Myaeng. Text genre classification with genre-revealing and subjectrevealing features. In Proc. 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages 145-150. ACM Press, 2002. ISBN 1-58113-561-0.
11. V. Levenshtein. Binary codes capable of correcting deletions insertions and reversals. ISov Phys Dokl, 6:707-710, 1966.
12. U. of Leipzig. Wortschatz. http://wortschatz.uni-leipzig.de, 1995.
13. G. Rehm. Towards AutomaticWeb Genre Identification. In Proceedings of the 35th Hawaii International Conference on System Sciences (HICSS'02). IEEE Computer Society, Jan. 2002.
14. D. Roussinov, K. Crowston, M. Nilan, B. Kwasnik, J. Cai, and X. Liu. Genre based navigation on the web. In Proceedings of the 34th Hawaii International Conference on System Sciences, 2001.
15. E. Stamatatos, N. Fakotakis, and G. Kokkinakis. Text genre detection using common word frequencies. In Proceedings of the 18th Int. Conference on Computational Linguistics, Saarbrücken, Germany, 2000.
16. University of Stuttgart. The decision tree tagger. http://www.ims.uni- stuttgart.de, 1996.