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Title:A Method to Automatically Identify the Results from Journal Articles
Author(s):Gabb, Henry A.; Lucic, Ana; Blake, Catherine
Subject(s):data analytics and evaluation
text/data/knowledge mining
Abstract:The idea of automating systematic reviews has been motivated by both advances in technology that have increased the availability of full-text scientific articles and by sociological changes that have increased the adoption of evidence-based medicine. Although much work has focused on automating the information retrieval step of the systematic review process with a few exceptions the information extraction and analysis have been largely overlooked. In particular, there is a lack of systems that automatically identify the results of an empirical study. Our goal in this paper is to fill that gap. We frame the problem as a classification task and employ three different objective, domain-independent feature selection strategies and two different classifiers. Additionally, special attention is paid to the selection of the data set used in this experiment, the feature selection metrics as well as the classification algorithms, and parameters of the algorithms used for classification in order to show the situatedness of this experiment and its dependence on each of the three parameters.
Issue Date:2015-03-15
Publisher:iSchools
Series/Report:iConference 2015 Proceedings
Genre:Conference Paper/Presentation
Type:Text
Language:English
URI:http://hdl.handle.net/2142/73645
Peer Reviewed:yes
Rights Information:Copyright 2015 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.
Date Available in IDEALS:2015-03-24


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