A new model based on the Bayesian approach has been developed which has interesting connections with the vector models of G. Salton. Theoretical details have been worked out. Ideally documents must be indexed by the """"""""real"""""""" objects that they refer to and these real objects become nodes in a system of multiple hierarchies called a specificity network. Each hierarchy is produced by a specificity operator and results in a tree of objects starting at the root with the most general and moving to greater specificity as one progresses towards the leaves. The objects which populate nodes are represented by textual terms or phrases. There may be many representations of any single object. The model described is labor intensive to construct if each document must be converted by hand to a form suitable to represent the objects discussed within it. Thus we are developing methods of automatic extraction of object representations. This will lead to a tractable task to represent documents. Current work is at the stage of theory and software development.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Intramural Research (Z01)
Project #
1Z01LM000021-09
Application #
6432747
Study Section
(CBB)
Project Start
Project End
Budget Start
Budget End
Support Year
9
Fiscal Year
2000
Total Cost
Indirect Cost
Name
National Library of Medicine
Department
Type
DUNS #
City
State
Country
United States
Zip Code
Wilbur, W John; Kim, Won (2009) The Ineffectiveness of Within - Document Term Frequency in Text Classification. Inf Retr Boston 12:509-525
Lu, Zhiyong; Kim, Won; Wilbur, W John (2009) Evaluating relevance ranking strategies for MEDLINE retrieval. J Am Med Inform Assoc 16:32-6
Lin, Jimmy; Wilbur, W John (2007) PubMed related articles: a probabilistic topic-based model for content similarity. BMC Bioinformatics 8:423
Wilbur, W John; Kim, Won; Xie, Natalie (2006) SPELLING CORRECTION IN THE PUBMED SEARCH ENGINE. Inf Retr Boston 9:543-564
Kim, W; Wilbur, W J (2001) Amino acid residue environments and predictions of residue type. Comput Chem 25:411-22
Aronson, A R; Bodenreider, O; Chang, H F et al. (2000) The NLM Indexing Initiative. Proc AMIA Symp :17-21
Wilbur, W J (2000) Boosting nai ve Bayesian learning on a large subset of MEDLINE. Proc AMIA Symp :918-22
Wilbur, W J; Neuwald, A F (2000) A theory of information with special application to search problems. Comput Chem 24:33-42
Wilbur, W J; Hazard Jr, G F; Divita, G et al. (1999) Analysis of biomedical text for chemical names: a comparison of three methods. Proc AMIA Symp :176-80