Mining HTML pages to support document sharing in a cooperative system

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

Volumn 2490 LNCS, Issue , 2002, Pages 420-434

  Malerba, Donato ^a   Esposito, Floriana ^a   Ceci, Michelangelo ^a  

^a UNIVERSITY OF BARI   (Italy)

Author keywords

Cooperative systems; Feature extraction and selection; Mining from semi structured data

Indexed keywords

FEATURE EXTRACTION; HTML; XML; INFORMATION MANAGEMENT;

CLIENT-SERVER APPLICATIONS; CO-OPERATIVE SYSTEMS; COMMON INTERESTS; DISTRIBUTED GROUPS; DOCUMENT SHARING; FEATURE EXTRACTION AND SELECTION; SELECTION TECHNIQUES; SEMI STRUCTURED DATA;

CLASSIFICATION (OF INFORMATION);

EID: 79951944332     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36128-6_25     Document Type: Conference Paper

References (20)

1. C. Apté, F. Damerau, & S.M. Weiss (1994). Automated learning of decision rules for text categorization. ACM Transactions on Information Systems, 12(3), 233-251.
2. G. Attardi, S. Di Marco, D. Salvi, & F. Sebastiani (1998). Categorisation by context. On-line Proceedings of the 1 International Workshop on Innovative Internet Information Systems, http://www.idt.ntnu.no/~monica/iii- 98/proceedings on line.html.
3. th Int. Conf. on Very Large Data Bases, 715-716.
4. C. Cleverdon (1984). Optimizing convenient online access to bibliographic databases. Information Services and Use, 4, 37-47.
5. th Int. Conf. on Document Analysis and Recognition ICDAR'01, IEEE Computer Society Press, Los Vaqueros, CA.
6. F. Esposito, D. Malerba, L. Di Pace, & P. Leo (2000). A Machine Learning Approach to Web Mining,. In E. Lamma & P. Mello (Eds.), AI*IA 99: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence, Vol. 1792, 190-201, Berlin: Springer.
7. th Int. Conf. on Machine Learning, 143-151.
8. th Int. Conf. on Machine Learning ICML'96, 284-292.
9. th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 298-306.
10. H. Luhn (1958). The automatic creation of literature abstracts. IBM Journal of Research and Development, 2(2):159-165.
11. B. Masand, G. Linoff, & D. Waltz (1992). Classifying new stories using memory based reasoning. Proc. SIGIR'92, 59-65.
12. D. Mladenic (1998). Feature subset selection in text-learning. In C. Nédellec, & C. Rouveirol (Eds.), Machine Learning: ECML-98, Lecture Notes in Artificial Intelligence, 1398, 95-100, Berlin: Springer.
13. S.K. Murthy, S. Kasif & S. Salzberg (1994). A system for induction of oblique decision trees. Journal of Artificial Intelligence Research, 2, 1-32.
14. M. Pazzani & D. Billsus (1997). Learning and revising user profiles: The identification of interesting web sites. Machine Learning Journal, 23, 313-331.
15. M. F. Porter (1980). An algorithm for suffix stripping. Program, 14(3): 130-137.
16. G. Salton (1989). Automatic text processing: The transformation, analysis, and retrieval of information by computer. Reading, MA: Addison-Wesley.
17. G. Salton & C. Buckley (1988). Term weighting approaches in automatic text retrieval. Information Processing and Management, 24(5), 513-523.
18. W.M. Shaw Jr (1995). Term-relevance computations and perfect retrieval performance. Information Processing & Management, 31(4), 491-498.
19. th Int. Conf. on Machine Learning ICML-97, 412-420.
20. G.K. Zipf (1949). Human Behavior and the Principle of Least Effort. Reading, MA: Addison-Wesley.