Please wait a minute...
New Technology of Library and Information Service  2004, Vol. 20 Issue (10): 24-32    DOI: 10.11925/infotech.1003-3513.2004.10.04
Current Issue | Archive | Adv Search |
Document Discrimination Modeling for Interface Design
Guillermo A. Oyarce
(School of Library and Information Sciences University of North Texas,USA)
Download: PDF(0 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  

Automatic methods for document analysis are particularly necessary in the Internet space. Indexing terms identify members of a class but constrain discrimination amongst them. The average frequency of a term over the document set is a simple function with an interesting visual friendly property which an interface could exploit for use
rs to easily manipulate and find the context of content bearing terms in the full text. This paper describes anexperiment that confirms some useful possibilities of this approach. Some implications for visualization are listed to help users analyze document sets and in the construction of contextual information.

Received: 06 July 2004      Published: 25 October 2004
Corresponding Authors: Guillermo A. Oyarce   
About author:: Guillermo A. Oyarce

Cite this article:

Guillermo A. Oyarce. Document Discrimination Modeling for Interface Design. New Technology of Library and Information Service, 2004, 20(10): 24-32.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2004.10.04     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2004/V20/I10/24

1  Belkin, N. J. and Croft, W. B., “Retrieval Techniques”, Annual Review of Information Science and Technology (ARIST) 22, p 109-145, 1987
2  Harman, D. (1993). Data Preparation. In R.H. Merchant (Ed.), Proceedings of the TIPSTER Text Program - Phase 1. Pages 17-31. San Francisco: Morgan Kaufaman.
3  Harman, D. (1996). Overview of the Fourth Text Retrieval Conference (TREC-4). In D.K. Harman (Ed.) The Fourth Text Retrieval Conference (TREC-4). Pages 1-23 Gaithersburg, MD: National Institute of Standards and Technology.
4  Hert, C. A. (1992). Exploring a New Model for Understanding Information Retrieval Interactions. Proceedings of the American Society for Information Science. 29, 72-75
5  Korfhage, R.; Lin X. and Dubin, D. (1995). VIRI: Visual Information Retrieval Interfaces. In Fox, E. et al. (Ed) Proceedings of the 18th. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Fox, E. Ed.) (pages 377). Danvers, MA: ACM
6  Kuhlthau, C.; Spink, A. and Cool, C. (1992).  Exploration into Stages in the Information Search Process in On-line Information Retrieval: Communication Between Users and Intermediaries. Proceedings of the American Society for Information Science, 29, 67-71
7  Rorvig, M. E.; Sullivan, T. and Oyarce, G. (1998) A Visualization Case Study of Feature Vector and Stemmer Effects on TREC Topic-document Subsets. http://129.120.9.76/asis98/
8  Salton, G. (1986). Another Look at Automatic Text-Retrieval Systems. Communications of the ACM. 1986 July; 29(7):648-656
9  Salton, G.; McGill, M. J. (1983). Introduction to Modern Information Retrieval. New York, NY: McGraw-Hill; 1983:400
10  Saracevic, T. and Kantor, P. (1991). Online Searching. Library Journal. 116(16), 47-51
11  Spark Jones, K. (1971). Automatic Keyword Classification for Information Retrieval. Archon Books. Connecticut
12  White, H., McCain, K. (1997). Visualization of Literatures. Annual Review of Information Science and Technology (ARIST), 32:99-168, 1997

 

No related articles found!
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn