Leveraging coreference to identify arms in medical abstracts: An experimental study

Performing systematic reviews is a critical yet manual, labor-intensive step in evidencebased medicine. Automating systematic reviews is an active area of research, requiring innovations in machine learning and computational linguistics. We examine how coreference resolution can aid in identifying the arms of a study, an often overlooked piece of information needed to synthesize the results in a systematic review. A classification model1 that performs better with the coreference features supports the intuition that coreference is able to capture the discourse salience of arms. We note that control arms do not benefit as much from these features.