Enhancing search engine quality using concept-based text retrieval

Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007

Volumn , Issue , 2007, Pages 26-34

  Shehata, Shady ^a   Karray, Fakhri ^a   Kamel, Mohamed ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SOFTWARE; INFORMATION SERVICES; INFORMATION THEORY; ONTOLOGY; SEARCH ENGINES; STATISTICAL METHODS;

ACCURATE ANALYSIS; CLASSICAL APPROACHES; CONCEPT-BASED; INTERNATIONAL CONFERENCES; NEW CONCEPT; QUALITY MEASURES; STATISTICAL ANALYSIS; TERM FREQUENCY; TEXT RETRIEVAL; TRADITIONAL TECHNIQUES; WEB INTELLIGENCE;

SEMANTICS;

EID: 48349122013     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WI.2007.51     Document Type: Conference Paper

References (19)

1. Apache Jakarta lucene search engine (version 1.3), http://lucene.apache. org/.
2. K. Aas and L. Eikvil. Text categorisation: A survey, technical report 941. Technical report, Norwegian Computing Center, June 1999.
3. N. Belkin and W. Croft. Retrieval techniques. Annual Review of Information Science and Technology, 22:109-145, 1987.
4. C. Buckley and E. M. Voorhees. Retrieval evaluation with incomplete information. In SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pages 25-32, New York, NY, USA, 2004. ACM Press.
5. R. Cole. Survey of the State of the Art in Human Language Technology (Studies in Natural Language Processing). Cambridge University Press, 1998.
6. M. Collins. Head-Driven Statistical Model for Natural Language Parsing. PhD thesis, University of Pennsylvania, 1999.
7. J. Fagan. The effectiveness of a nonsyntactic approach to automatic phrase indexing for document retrieval. Journal of the American Society for Information Science, 40(2): 115-132, 1989.
8. C.Fillmore. The case for case. Chapter in: Universals in Linguistic Theory. Holt, Rinehart and Winston, Inc., New York, 1968.
9. D. Gildea and D. Jurafsky. Automatic labeling of semantic roles. Computational Linguistics, 28(3):245-288, 2002.
10. D. Hull. Using statistical testing in the evaluation of retrieval experiments. In Proceedings of Special Interest Group on Information Retrieval (ACM SIGIR), 1993.
11. D. Jurafsky and J. H. Martin. Speech and Language Processing. Prentice Hall Inc., 2000.
12. P. Kingsbury and M. Palmer. Propbank: the next level of treebank. In Proceedings of Treebanks and Lexical Theories, 2003.
13. M. F. Porter. An algorithm for suffix stripping. Program, 14(3): 130-137, July 1980.
14. S. Pradhan, K. Hacioglu, V. Krugler, W. Ward, J. H. Martin, and D. Jurafsky. Support vector learning for semantic argument classification. Machine Learning, 60(1-3):11-39, 2005.
15. S. Pradhan, K. Hacioglu, W. Ward, J. H. Martin, and D. Jurafsky. Semantic role parsing: Adding semantic structure to unstructured text. In Proceedings of the 3th IEEE International Conference on Data Mining (ICDM), pages 629-632, 2003.
16. S. Pradhan, W. Ward, K. Hacioglu, J. Martin, and D. Jurafsky. Shallow semantic parsing using support vector machines. In Proceedings of the Human Language Technology/North American Association for Computational Linguistics (HLT/NAACL), 2004.
17. G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.
18. G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. Communications of the ACM, 18(11):112-117, 1975.
19. S. Shehata, F. Karray, and M. Kamel. Enhancing text retrieval performance using conceptual ontological graph. In ICDM Workshops, pages 39-44, 2006.