Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Extracting the names of genes and gene products with a hidden Markov model
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Biomedical named entity recognition using two-phase model based on SVMs
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Term identification in the biomedical literature
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Overview of results of the MUC-6 evaluation
MUC6 '95 Proceedings of the 6th conference on Message understanding
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Tuning support vector machines for biomedical named entity recognition
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Effective adaptation of a Hidden Markov Model-based named entity recognizer for biomedical domain
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Improving the scalability of semi-Markov conditional random fields for named entity recognition
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Exploiting context for biomedical entity recognition: from syntax to the web
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
How to make the most of NE dictionaries in statistical NER
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Brief Communication: Two-phase biomedical named entity recognition using CRFs
Computational Biology and Chemistry
A composite kernel for named entity recognition
Pattern Recognition Letters
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In this paper, we describe a two-phase method for biomedical named entity recognition consisting of term boundary detection and biomedical category labeling. The term boundary detection can be defined as a task to assign label sequences to a given sentence, and biomedical category labeling can be viewed as a local classification problem which does not need knowledge of the labels of other named entities in a sentence. The advantage of dividing the recognition process into two phases is that we can measure the effectiveness of models at each phase and select separately the appropriate model for each subtask. In order to obtain a better performance in biomedical named entity recognition, we conducted comparative experiments using several learning methods at each phase. Moreover, results by these machine learning based models are refined by rule-based postprocessing. We tested our methods on the JNLPBA 2004 shared task and the GENIA corpus.