In this paper, we explore how to adapt a general Hidden Markov Model-based named entity recognizer effectively to biomedical domain. We integrate various features, including simple deterministic features, morphological features, POS features and semantic trigger features, to capture various evidences especially for biomedical named entity and evaluate their contributions. We also present a simple algorithm to solve the abbreviation problem and a rule-based method to deal with the cascaded phenomena in biomedical domain. Our experiments on GENIA V3.0 and GENIA V1.1 achieve the 66.1 and 62.5 F-measure respectively, which outperform the previous best published results by 8.1 F-measure when using the same training and testing data.
For example, REF , and employed a Hidden Markov Model to GENIA to extract inner entities and then used rule-based methods to obtain the outer entities.
Effective Adaptation Of Hidden Markov Model-Based Named Entity Recognizer For Biomedical Domain
Workshop On Natural Language Processing In Biomedicine
Computer Science