Shallow parsing using specialized hmms
The Journal of Machine Learning Research
Enhancing HMM-based biomedical named entity recognition by studying special phenomena
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Comparison of character-level and part of speech features for name recognition in biomedical texts
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
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
Introduction to the bio-entity recognition task at JNLPBA
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Named entity recognition in biomedical texts using an HMM model
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploring deep knowledge resources in biomedical name recognition
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
Feature selection techniques for maximum entropy based biomedical named entity recognition
Journal of Biomedical Informatics
A composite kernel for named entity recognition
Pattern Recognition Letters
Stacked ensemble coupled with feature selection for biomedical entity extraction
Knowledge-Based Systems
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With a recent quick development of a molecular biology domain it becomes indispensable to promote different resources as databases and ontologies that represent the formal knowledge of the domain. As these resources have to be permanently updated, due to a constant appearance of new data, the Information Extraction (IE) methods become very useful. Named Entity Recognition (NER), that is considered to be the easiest task of IE, still remains very challenging in molecular biology domain because of the special phenomena of biomedical entities. In this paper we present our Hidden Markov Model (HMM)-based biomedical NER system that takes into account only parts-of-speech as an additional feature, which are used both to tackle the problem of nonuniform distribution among biomedical entity classes and to provide the system with an additional information about entity boundaries. Our system, in spite of its poor knowledge, has proved to obtain better results than some of the state-of-the-art systems that employ a greater number of features.