How to interpret PubMed queries and why it matters

Journal of the American Society for Information Science and Technology

Volumn 60, Issue 2, 2009, Pages 264-274

  Yeganova, Lana ^a   Comeau, Donald C ^a   Kim, Won ^a   John Wilbur, W ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ON-MACHINES; PROXIMITY SEARCHING; QUERY TERMS; RETRIEVAL EVALUATIONS; RETRIEVAL QUALITIES; SEARCH TERMS;

LEARNING ALGORITHMS; SEARCH ENGINES;

LEARNING SYSTEMS;

EID: 60549096298     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.20979     Document Type: Article

References (14)

1. Beigbeder, M., & Mercier, A. (2005). An information retrieval model using the fuzzy proximity degree of term occurences. Proceedings of the 2005 ACM Symposium on Applied Computing (SAC) (pp. 1018-1022). New York: ACM.
2. Buttcher, S., Clarke, C. et al. (2006). Term proximity scoring for ad-hoc retrieval on very large text collections. Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 621-622). New York: ACM.
3. Clarke, C., Cormack, G., & Burkowski, F. (1995). Shortest substring ranking. In K. Harman (Ed.), Proceedings of Fourth Text Retrieval Conference (TREC-4) (pp. 295-304). Darby, PA: Diane Publishing.
4. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861-874.
5. Haas, S., & Losee, R. (1994). Looking in the text windows: Their size and composition. Information Processing and Management, 30(5), 619-629.
6. Keen, E.M. (1992). Some aspects of proximity searching in text retrieval systems. Journal of Information Science, 18(2), 89-98.
7. Larson, H.J. (1982). Introduction to probability theory and statistical inference. New York: John Wiley & Sons.
8. Lewis, D.D. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval. Proceedings of the 10th European Conference on Machine Learning. Retrieved October 21, 2008, from https://epri.nts. kfupm.edu.sa/52684/1/52684.pdf
9. Ruthven, I. & Lalmas, M. (2003). A survey on the use of relevance feedback for information access systems. The Knowledge Engineering Review, 18(2), 95-145.
10. Saidaty, M., Shu, J., & Knaus, W. (2007). Relemed: sentence-level search, engine with relevance score for the MEDMLINE database of biomedical abstracts. BMC Medical Informatics and Decision Making, 7(1).
11. Saracevic, T. (2007a). Relevance: A review of the literature and a framework for thinking on the notion in information science. Part II: Nature and manifestations of relevance. Journal of the American Society for Information Science, 58(13), 1915-1933.
12. Saracevic, T. (2007b). Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance. Journal of the American Society for Information Science, 58(13), 2126-2144.
13. Sohn, S., Kim, W., Comeau, D.C., & Wilbur, W.J. (2008). Optimal training sets for Bayesian prediction of MeSH assignment. Journal of the American Medical Informatics Association, 15(4), 546-553.
14. Voorhees, E. (2001). The philosophy of information retrieval evaluation. Lecture Notes in Computer Science,Volume 2406 (pp. 355-370). New York: ACM.