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 non-uniform 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.
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