Most information extraction (IE) systems treat separate potential extractions as independent. However, in many cases, considering influences between different potential extractions could improve overall accuracy. Statistical methods based on undirected graphical models, such as conditional random fields (CRFs), have been shown to be an effective approach to learning accurate IE systems. We present a new IE method that employs Relational Markov Networks, which can represent arbitrary dependencies between extractions. This allows for ``collective information extraction'' that exploits the mutual influence between possible extractions. Experiments on learning to extract protein names from biomedical text demonstrate the advantages of this approach.

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In Proceedings of the ICML-04 Workshop on Statistical Relational Learning and its Connections to Other Fields, Banff, Alberta, July 2004.

@InProceedings{bunescu:srl04, title={Relational Markov Networks for Collective Information Extraction}, author={Razvan Bunescu and Raymond J. Mooney}, booktitle={Proceedings of the ICML-04 Workshop on Statistical Relational Learning and its Connections to Other Fields}, month={July}, address={Banff, Alberta}, key={RMN, IE, BIODM}, url="http://www.cs.utexas.edu/users/ai-lab?bunescu:srl04", year={2004} }