Automatically extracting knowledge from online repositories (e.g., PubMed) holds the promise of dramatically speeding up biomedical research and drug design, and represents an outstanding example for the great vision of knowledge extraction from the Web. After initially focusing on entity recognition and binary interaction for protein, the community has recently shifted their attention towards the more ambitious goal of recognizing complex, nested event structures, which are ubiquitous in the literature. However, the state-of-the-art systems still adopt a pipeline architecture and fail to leverage the relational structures among candidate entities for mutual disambiguation. In this paper, we present the first joint approach for bioevent extraction that obtains state-of-the-art results. Our system is based on Markov logic and jointly predicts events and their arguments. We evaluated it using the BioNLP-09 Shared Task and compared it to the participating systems. Experimental results demonstrate the advantage of our approach.
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