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An automata based approach to biomedical named entity recognition

By James Dowdall, Bill Keller, Lluis Padro and Muntsa Padro


ing an automata learning algorithm: Causal-State Splitting Reconstruction\ud [1]. This algorithm has previously been applied to Named Entity Recognition [2]\ud obtaining good results given the simplicity of the approach.\ud The same approach has been applied to Biomedical NE identiļ¬cation, using\ud GENIA corpus 3.0, with 10-fold cross-validation. Our system attained F1 =\ud 73.14%.\ud These results can be compared directly to [3] and [4], which used the same\ud data. First system obtains F1 = 57.4% using ME Models, and the second one reports F1 = 79.2% using SVMs. Both improve their results using post-processing\ud techniques, reaching F1 = 76.9% and F1 = 79.9% respectively.\ud Our system does not use any post-processing techniques, and takes into\ud acount few features, so the results are considered very promising. In future work\ud some post-processing will be developed to improve the results

Topics: P0098, QA75
Year: 2007
OAI identifier:
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