Proceedings of the ICML-2003 Workshop on Machine Learning in Bioinformatics, pp.46-53, Washington DC, August 2003.
Paper ID: 126
Category: Bioinformatics, Information Extraction, Natural Language Learning
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.
