Training the genre classifier for automatic classification of web pages

Proceedings of the International Conference on Information Technology Interfaces, ITI

Volumn , Issue , 2007, Pages 93-98

  Vidulin, Vedrana ^a   Luštrek, Mitja ^a   Gams, Matjaž ^a  

^a JO EF STEFAN INSTITUTE   (Slovenia)

Author keywords

Ensemble algorithm; Genre classification; Genre features; Web page

Indexed keywords

AUTOMATIC CLASSIFICATION; DATA SETS; ENSEMBLE ALGORITHM; F-MEASURE; GENRE CLASSIFICATION; GENRE FEATURES; INTERNATIONAL CONFERENCES; MACHINE-LEARNING ALGORITHMS; WEB PAGE; WEB PAGES;

ARTIFICIAL INTELLIGENCE; AUTOMATIC INDEXING; CLASSIFICATION (OF INFORMATION); COMPUTER SOFTWARE; DECISION MAKING; DECISION THEORY; DECISION TREES; INFORMATION SERVICES; INFORMATION TECHNOLOGY; LEARNING SYSTEMS; SEARCH ENGINES; SET THEORY; TECHNOLOGY; TREES (MATHEMATICS); WORLD WIDE WEB;

LEARNING ALGORITHMS;

EID: 48349128816     PISSN: 13301012     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ITI.2007.4283750     Document Type: Conference Paper

References (24)

1. Alvis; 2007 http://www.alvis.info/alvis/ [01/23/2007]
2. Argamon S, Koppel M, Avneri G. Routing Documents According to Style. First International Workshop on Innovative Information Systems; 1998.
3. Berners-Lee T, Fielding RT, Masinter L. Uniform Resource Identifier (URI): Generic Syntax. Internet Society. RFC 3986; STD 66; 2005.
4. Buntine W. Private communication; 2007.
5. Dewdney N, VanEss-Dykema C, MacMillan R. The Form is the Substance - Classifications of Genres in Text; 1998.
6. Finn A. Machine Learning for Genre Classification; 2002.
7. Karlgren J. Stylistic Experiments for Information Retrieval. PhD thesis; 2000.
8. Karlgren J, Cutting D. Recognizing Text Genres with Simple Metrics Using. Proceedings of the 15th. International Conference on Computational Linguistics; 1994.
9. Kessler B, Nunberg G, Schütze H. Automatic Detection of Text Genre. 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; 1997.
10. Kohavi R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. IJCAI; 1995.
11. Large A, Tedd LA, Hartley RJ. Information seeking in the online age: Principles and practice. London: Bowker; 1999.
12. Lim CS, Lee KL, Kim GC. Multiple sets of features for automatic genre classification of web documents. Information Processing & Management; 2005.
13. Mitchell TM. Machine Learning. McGraw-Hill; 1997.
14. Porter M. The Porter Stemming Algorithm; 2007, http://www.tartarus.org/ ~martin/PorterStemmer/ [01/10/2007]
15. Quinlan JR. C4.5: Programs for Machine Learning. Morgan Kaufmann; 1993.
16. Santini M. A Shallow Approach to Syntactic Feature Extraction for Genre Classification. Proceedings of the 7th Annual Colloquium for the UK Special Interest Group for Computational Linguistics; 2003.
17. Santini M. Common Criteria for Genre Classification: Annotation and Granularity. Workshop on Text-based Information Retrieval (TIR-06). In Conjunction with ECAI 2006. Riva del Garda; 2006.
18. Santini M. Description of 3 feature sets for automatic identification of genres in web pages; 2006. http://www.itri.brighton.ac.uk/~Marina.Santini/ three_feature_sets.pdf [12/19/2006]
19. Stamatatos E, Kokkinakis G, Fakotakis N. Automatic Text Categorization in Terms of Genre and Author. Computational Linguistics; 2000.
20. Vidulin V, Luštrek M, Gams M. Comparison of the Performance of Genre Classifiers Trained by Different Machine Learning Algorithms; Information Society. Ljubljana; 2006.
21. Web Genre Dataset; 2007. http://dis.ijs.si/mitjal/genre/ [04/30/2007]
22. Wikipedia - Information retrieval; 2007. http://en.wikipedia.org/wiki/ Information_retrieval [01/28/2007]
23. Witten IH, Frank E. Data Mining - Practical Machine Learning Tools and Techniques. Elsevier Inc.; 2005.
24. Zeitgeist;http://www.google.com/press/zeitgeist.html [06/25/2005]