We present results from a variety of learned information extraction systems for identifying human protein names in Medline abstracts and subsequently extracting interactions between the proteins. We demonstrate that machine learning approaches using support vector machines and hidden Markov models are able to identify human proteins with higher accuracy than several previous approaches. We also demonstrate that various rule induction methods are able to identify protein interactions with higher precision than manually-developed rules.

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In Proceedings of the ICML-03 Workshop on Machine Learning in Bioinformatics, pp. 46-53, Washington, DC, August 2003.

@inproceedings{bunescu:ml03-wkshp, title={Learning to Extract Proteins and their Interactions from Medline Abstracts}, author={Razvan Bunescu and Ruifang Ge and Rohit J. Kate and Raymond J. Mooney and Yuk Wah Wong and Edward M. Marcotte and Arun Kumar Ramani}, booktitle={Proceedings of the ICML-03 Workshop on Machine Learning in Bioinformatics}, month={August}, address={Washington, DC}, pages={46-53}, url="http://www.cs.utexas.edu/users/ai-lab?bunescu:ml03-wkshp", year={2003} }