Learning to recognize webpage genres

Information Processing and Management

Volumn 45, Issue 5, 2009, Pages 499-512

  Kanaris, Ioannis ^a   Stamatatos, Efstathios ^a  

^a UNIVERSITY OF THE AEGEAN   (Greece)

Author keywords

Character n grams; Web genre classification; Webpage representation

Indexed keywords

AUTOMATED EXTRACTION; AUTOMATIC DETECTION; BINARY REPRESENTATIONS; CHARACTER N-GRAMS; CHECK EXPERIMENT; E-SHOPS; FEATURE SETS; FEATURE TYPES; GENRE DETECTION; HOME PAGE; HTML TAGS; INFORMATION NEED; N-GRAMS; NOISY ENVIRONMENT; VARIABLE LENGTH; WEB GENRE CLASSIFICATION; WEB-PAGE; WEBPAGE REPRESENTATION;

ABILITY TESTING; MARKUP LANGUAGES; SEARCH ENGINES; SOFTWARE AGENTS;

FEATURE EXTRACTION;

EID: 67650140659     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ipm.2009.05.003     Document Type: Article

References (35)

1. Bekkerman, R. (2008). Combinatorial Markov random fields and their applications to information organization. Ph.D. Thesis, University of Massachusetts Amherst.
2. Boese, E., & Howe, A. (2005). Effects of web document evolution on genre classification. In Proceedings of the ACM 14th conference on information and knowledge management.
3. Braslavski, P. (2007). Combining relevance and genre-related rankings: An exploratory study. In Proceedings of the international workshop towards genre-enabled search engines: The impact of NLP (pp. 1-4).
4. Clark, M., & Watt, S. (2007). Classifying XML documents by using genre features. In Proceedings of the 4th international workshop on text-based information retrieval.
5. Dewdney, N., VanEss-Dykema, C., & MacMillan, R. (2001). The form is the substance: classification of genres in text. In Workshop on human language technology and knowledge management (39th ACL-10th EACL) (pp. 53-61).
6. Dong, L., Watters, C., Duffy, J., & Shepherd, M. (2008). An examination of genre attributes for web page classification. In Proceedings of the 41st annual Hawaii international conference on system sciences (HICSS 2008).
7. Finn A., and Kushmerick N. Learning to classify documents according to genre. Journal of the American Society for Information Science and Technology 7 5 (2006) 1506-1518
8. Forman G. An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research 3 (2003) 1289-1305
9. Houvardas, J., & Stamatatos, E. (2006). N-gram feature selection for authorship identification. In Proceedings of the 12th international conference on artificial intelligence: methodology, systems, applications, LNCS, 4183 (pp. 77-86). Springer.
10. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the 10th European conference on machine learning (pp. 137-142).
11. Kennedy, A., & Shepherd, M. (2005). Automatic identification of home pages on the web. In Proceedings of the 38th Hawaii international conference on system sciences.
12. Keselj, V., Peng, F., Cercone, N., & Thomas, C. (2003). N-gram-based author profiles for authorship attribution. In Proceedings of the conference of pacific association for computational linguistics.
13. Kessler, B., Nunberg, G., & Schütze, H. (1997). Automatic detection of text genre. In Proceedings of the 35th annual meeting of the association for computational linguistics (pp. 32-38).
14. Kim, Y., & Ross, S. (2007). Variations of word frequencies in genre classification tasks. In Proceedings of the DELOS conference on digital libraries.
15. Kim, Y., & Ross, S. (2008). Examining variations of prominent features in genre classification. In Proceedings of the 41st annual Hawaiian international conference on system sciences (HICSS 2008).
16. Lee, Y. B., & Myaeng, S. H. (2002). Text genre classification with genre-revealing and subject-revealing features. In Proceedings of the 25th ACM SIGIR conference on research and development in information retrieval (pp. 145-150).
17. Levering, R., Cutler, M., & Yu, L. (2008). Using visual features for fine-grained genre classification of web pages. In Proceedings of the 41st Hawaii international conference on system sciences.
18. Lim C.S., Lee K.J., and Kim G.C. Multiple sets of features for automatic genre classification of web documents. Information Processing and Management 41 5 (2005) 1263-1276
19. Mason, J. E., Shepherd, M., & Duffy, J. (2009). An N-gram based approach to automatically identifying web page genre. In Proceedings of the 42nd Hawaii international conference on system sciences.
20. Meyer zu Eissen, S. (2007). On information need and categorizing search. Ph.D. Thesis, Department of Computer Science, University of Paderborn.
21. Meyer zu Eissen S., and Stein B. Genre classification of web pages: User study and feasibility analysis. In: Biundo S., Fruhwirth T., and Palm G. (Eds). KI 2004: Advances in artificial intelligence (2004), Springer 256-269
22. Rehm, G., Santini, M., Mehler, A., Braslavski, P., Gleim, R, Stubbe, A., Symonenko, S., Tavosanis, M., & Vidulin, V. (2008). Towards a reference corpus of web genres for the evaluation of genre identification systems. In Proceedings of the 6th international conference on language resources and evaluation (LREC 2008).
23. Robnik-Sikonja M., and Kononenko I. Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning 53 1-2 (2003) 23-69
24. Rosso, M. (2005). Using genre to improve web search. Ph.D. Thesis, University of North Carolina at Chapel Hill.
25. Santini, M. (2007). Automatic identification of genre in webpages. Ph.D. Thesis, University of Brighton.
26. Santini M. Zero, single, or multi? Genre of web pages through the users' perspective. Information Processing and Management 44 2 (2008) 702-737
27. Santini, M. (2008b). Cross-testing a genre classification model. In Proceedings of the 2nd Swedish language technology conference (SLTC 2008) (pp. 7-8).
28. Sebastiani F. Machine learning in automated text categorization. ACM Computing Surveys 34 1 (2002)
29. Shepherd, M., & Watters, C. (1998). The Evolution of Cybergenres. In Proceedings of the 31st Hawaii international conference on system sciences.
30. Silva, J., & Lopes, G. (1999). A local maxima method and a fair dispersion normalization for extracting multiword units. In Proceedings of the 6th meeting on the mathematics of language (pp. 369-381).
31. Silva, J., Dias, G., Guilloré, S., & Lopes, G. (1999). Using localmaxs algorithm for the extraction of contiguous and non-contiguous multiword lexical units. Lecture notes on artificial intelligence, 1695 (pp. 113-132). Springer.
32. Stamatatos, E. (2006). Ensemble-based author identification using character N-grams. In Proceedings of 3rd international workshop on text-based information retrieval (pp. 41-46).
33. Stamatatos, E., N. Fakotakis, & G. Kokkinakis (2000). Text genre detection using common word frequencies. In Proceedings of the 18th international conference on computational linguistics (pp. 808-814).
34. Swales J. Genre analysis: English in academic and research settings (1990), Cambridge University Press
35. Vidulin, V., Lustrek, M., & Gams, M. (2007). Using genres to improve search engines. In Proceedings of the international workshop towards genre-enable search engines: the impact of natural language processing.