Extraction of keyterms by simple text mining for business information retrieval

Proceedings - ICEBE 2005: IEEE International Conference on e-Business Engineering

Volumn 2005, Issue , 2005, Pages 332-339

  Gao, Xiangzhu ^a   Murugesan, San ^a   Lo, Bruce ^b  

^a SOUTHERN CROSS UNIVERSITY   (Australia)

^b UNIVERSITY OF WISCONSIN EAU CLAIRE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DATA REDUCTION; INFORMATION RETRIEVAL; LINGUISTICS; STATISTICS;

BUSINESS INFORMATION RETRIEVAL; TEXT MINING;

TEXT PROCESSING;

EID: 33845476272     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICEBE.2005.66     Document Type: Conference Paper

References (26)

1. Hawking, D., Crimmins, F., Craswell, N. & Upstill, T. 2004. How Valuable is External Link Evidence when Searching Enterprise Webs?. Fifteenth Australasian Database Conference (ADC2004), Dunedin, NZ, pp.77-84.
2. Miller, T.W. 2005. Data and Text Mining: A Business Applications Approach. New Jersey: Pearson Prentice Hall.
3. Mukherjee, R. & Mao, J. 2004. Enterprise Search: Tough Stuff. ACM Queue, 2(2):37-46.
4. Rao, R. 2004. From IR to search and beyond. ACM Queue, 2(3):67-73.
5. Moulton, L. 2004. Understanding Taxonomies & Search for Corporate Applications. The Gilbane Report, 12(4):2-12.
6. Koster and Seutter, 2003. Taming wild phrases, Proceedings of the 25th European Conference on IR Research (ECIR 2003), Pisa, pp161-176.
7. Pickens, J. and Croft, W.B. 2000. An exploratory analysis of phrases in text retrieval. RIAO 2000 Conference, Paris, pp1179-1195.
8. Jiang, M., Jensen, E., Beitzel, S. & Argamon, S. 2004. Effective use of phrases in language modelling to improve information retrieval, Symposium on AI & Math Special Session on Intelligent Text Processing, Florida.
9. Caropreso, M.F., Matwin, S. and Sebastiani, F. 2000. Statistical phrases in automated text categorization, Technical Report IEI-B4-07-2000, Istituto di Elaborazione dell'Informazione, Consiglio Nazionale del le Ricerche.
10. Tseng, Y. 2002. Automatic Thesaurus Generation for Chinese Documents. Journal of the American Society for Information Science and Technology, 53(13):1130-1138.
11. Zhou, L. & Zhang, D. 2003. NLPIR: A Theoretical Framework for Applying Natural Language Processing to Information Retrieval. Journal of the American Society for Information Science and Technology, 54(2): 115-123.
12. Nakashima, M., Sato, K., Qu, Y. & Ito, T. 2003. Browsing-Based Conceptual Information Retrieval Incorporating Dictionary Term Relations, Keyword Association, and a User's Interest, Journal of the American Society for Information Science and Technology, 54(1): 16-28.
13. Yang, M., Yang, C.C. & Chung, Y. 1997. A natural language processing based Internet agent. Proceedings of the IEEE International Conference on Multimedia Computing and Systems, Orlando, Florida, USA, pp. 100-105.
14. Baeza-Yates, R. & Ribeiro-Neto, B. 1999, Modern Information Retrieval. Harlow, England: Addison Wesley.
15. Greiff, W.R. 1999. Maximum entropy, weight of evidence and information retrieval. Department of Computer Science, University of Massachusetts.
16. Strzalkowski, T. 1997. Robust text processing in automated information retrieval. Readings in Information Retrieval, Morgan Kaufman Publishers, pp317-322.
17. Peñas, A. Gonzalo, J. & Verdejo, F. 2001. Cross-language information access through phrase browsing. Proceedings of the 6th International Workshop on Applications of Natural Language to Information Systems, Madrid, pp 121-130.
18. Gao, X. & Lo, B. 2001. Measuring document relevance in query space. Proceedings of the Asia Pacific Web Conference 2001, Changsha, China, pp.51-58.
19. Gao, X., Murugesan, S. & Lo, B. 2003. A Dynamic Information Retrieval System for the Web. Proceedings of the 27th International Computer Software and Applications Conference, Dallas, Texas, USA, pp. 663-667.
20. Gao, X. 2004. A non-indexing approach to information retrieval. Thesis, School of Multimedia and Information Technology, Southern Cross University.
21. Jones, K.S. 1999. What is the role of NLP in text retrieval?. In T. Strzalkowski, editor, Natural Language Information Retrieval, pp. 1-24, Kluwer Academic, Dordrecht.
22. Karlgren, J. 1999. The Basics of Information Retrieval, [On-line] Available at http://citeseer.nj.nec.com/200523.hrml.
23. Rus, D. & Subramanian, D. 1997. Customizing Information Capture and Access. ACM Transactions on Information Systems, 15(1):67-101.
24. Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C. and Nevill-Manning, C.G. 2000. KEA: Practical automatic keyphrase extraction. Working paper 00/5, Department of Computer Science, The University of Waikato.
25. Turney, P.D. 2003. Coherent keyphrase extraction via Web mining. Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-03), Acapulco, Mexico, pp.434-439.
26. Turney, P.D. 1999. Learning to extract keyphrases from text, Technical Report ERB-1057, National Research Council, Institute for Information Technology.