Conditional Random Fields vs. Hidden Markov Models in a biomedical Named Entity Recognition task
Natalia Ponomareva, Paolo Rosso, Ferran Plà, Antonio Molina · 2008
With a recent quick development of a molecular biology domain 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 complex structure of biomedical entities and the lack of naming convention. In this paper we apply two popular sequence labeling approaches: Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs) to solve this task. We exploit different stategies to construct our biomedical Named Entity (NE) recognizers which take into account special properties of each approach. Although the CRF-based model has obtained much better results in the F-score, the advantage of the CRF approach remains disputable, since the HMM-based model has achieved a greater recall for some biomedical classes. This fact makes us think about a possibility of an effective combination of these models.