A comparative analysis of retrieval features used in the TREC 2006 Genomics Track passage retrieval task.
OBJECTIVE
Identify the set of features that best explained the variation in the performance measure of TREC 2006 Genomics information extraction task, Mean Average Passage Precision (MAPP).


METHODS
A multivariate regression model was built using a backward-elimination approach as a function of certain generalized features that were common to all the algorithms used by TREC 2006 Genomics track participants.


RESULTS
Our regression analysis found that the following four factors were collectively associated with variation in MAPP: (1) Normalization of keywords in the query (2) Use of Entrez gene thesaurus for synonymous terms look-up (3) Unit of text retrieved using respective IR algorithms and (4) The way a passage was defined.


CONCLUSIONS
These reasonably likely hypotheses, generated by an exploratory data analysis, are informative in understanding results of the TREC 2006 Genomics passage extraction task. This approach has general value for analyzing the results of similar common challenge tasks.
Rekapalli
Hari Krishna
HK
Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA.
Cohen
Aaron M
AM
Hersh
William R
WR
D000043:Abstracting and Indexing; D000465:Algorithms; D019992:Databases as Topic; D023281:Genomics; D016247:Information Storage and Retrieval; D015999:Multivariate Analysis; D012044:Regression Analysis; D013358:Subject Headings; D018875:Vocabulary, Controlled