The Biomedical Discourse Relation Bank

Background: Identification of discourse relations, such as causal and contrastive relations, between situations mentioned in text is an important task for biomedical text-mining. A biomedical text corpus annotated with discourse relations would be very useful for developing and evaluating methods for biomedical discourse processing. However, little effort has been made to develop such an annotated resource. Results: We have developed the Biomedical Discourse Relation Bank (BioDRB), in which we have annotated explicit and implicit discourse relations in 24 open-access full-text biomedical articles from the GENIA corpus. Guidelines for the annotation were adapted from the Penn Discourse TreeBank (PDTB), which has discourse relations annotated over open-domain news articles. We introduced new conventions and modifications to the sense classification. We report reliable inter-annotator agreement of over 80% for all sub-tasks. Experiments for identifying the sense of explicit discourse con...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Rashmi Prasad, Susan McRoy, Nadya Frid, Aravind K. Joshi, Hong Yu
• Keywords: Biometrics, BMCBI 2011, Classification Accuracy, Discourse Relations, Sense Disambiguation