Year: 2015
Type: article
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. More specifically, we focus on the identification of 1) any claim in an article and on the identification of 2) explicit claims, a subtype of a more general claim. We frame the problem as a classification task and employ three different domain-independent feature selection strategies (χ2 statistic, information gain, and mutual information) with two different classifiers [support vector machines (SVM) and naïve Bayes (NB)]. With respect to both accuracy and F1, the χ2 statistic and information gain consistently outperform mutual information. The SVM and NB classifiers had similar accuracy when predicting any claim but NB had better F1 performance for explicit claims. Lastly, we explored a semantic model developed for a different dataset. Accuracy was lower for the semantic model but when used with SVM plus sentence location information,
Authors: Henry A. Gabb, Ana Lučić, Catherine Blake
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W758475925