A semi-supervised approach using label propagation to support citation screening.

ABSTRACT: Citation screening, an integral process within systematic reviews that identifies citations relevant to the underlying research question, is a time-consuming and resource-intensive task. During the screening task, analysts manually assign a label to each citation, to designate whether a citation is eligible for inclusion in the review. Recently, several studies have explored the use of active learning in text classification to reduce the human workload involved in the screening task. However, existing approaches require a significant amount of manually labelled citations for the text classification to achieve a robust performance. In this paper, we propose a semi-supervised method that identifies relevant citations as early as possible in the screening process by exploiting the pairwise si

SUBMITTER: Kontonatsios G 

PROVIDER: S-EPMC5726085 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature