Paper: Classifying Negative Findings in Biomedical Publications

ACL ID W14-3403
Title Classifying Negative Findings in Biomedical Publications
Venue Proceedings of the BioNLP Shared Task 2013 Workshop
Session
Year 2014
Authors

Publication bias refers to the phenome- non that statistically significant, ?posi- tive? results are more likely to be pub- lished than non-significant, ?negative? results. Currently, researchers have to manually identify negative results in a large number of publications in order to examine publication biases. This paper proposes an NLP approach for automati- cally classifying negated sentences in bi- omedical abstracts as either reporting negative findings or not. Using multino- mial na?ve Bayes algorithm and bag-of- words features enriched by parts-of- speeches and constituents, we built a classifier that reached 84% accuracy based on 5-fold cross validation on a bal- anced data set.