Analysis of the effect of negation on information retrieval of medical data
Koopman, Bevan, Bruza, Peter D., Sitbon, Laurianne, & Lawley, Michael J. (2010) Analysis of the effect of negation on information retrieval of medical data. In Proceedings of 15th Australasian Document Computing Symposium (ADCS), University of Melbourne, University of Melbourne, Melbourne, Victoria.
Most information retrieval (IR) models treat the presence of a term within a document as an indication that the document is somehow "about" that term, they do not take into account when a term might be explicitly negated. Medical data, by its nature, contains a high frequency of negated terms - e.g. "review of systems showed no chest pain or shortness of breath".
This papers presents a study of the effects of negation on information retrieval. We present a number of experiments to determine whether negation has a significant negative affect on IR performance and whether language models that take negation into account might improve performance. We use a collection of real medical records as our test corpus. Our findings are that negation has some affect on system performance, but this will likely be confined to domains such as medical data where negation is prevalent.
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|Item Type:||Conference Paper|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
Past > Schools > Information Systems
|Copyright Owner:||Copyright 2010 [please consult the authors]|
|Deposited On:||18 Nov 2010 10:32|
|Last Modified:||01 Mar 2012 00:27|
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