Paper: Joint Inference for Knowledge Extraction from Biomedical Literature

ACL ID N10-1123
Title Joint Inference for Knowledge Extraction from Biomedical Literature
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

Knowledge extraction from online reposito- ries such as PubMed holds the promise of dramatically speeding up biomedical research and drug design. After initially focusing on recognizing proteins and binary interactions, the community has recently shifted their at- tention to the more ambitious task of recogniz- ing complex, nested event structures. State-of- the-art systems use a pipeline architecture in which the candidate events are identified first, and subsequently the arguments. This fails to leverage joint inference among events and arguments for mutual disambiguation. Some joint approaches have been proposed, but they still lag much behind in accuracy. In this pa- per, we present the first joint approach for bio- event extraction that obtains state-of-the-art results. Our system is ...