Despite an increasing amount of research on biomedical named entity recognition, there has been not enough work done on disease mention recognition. Difficulty of obtaining adequate corpora is one of the key reasons which hindered this particular research. Previous studies argue that correct identification of disease mentions is the key issue for further improvement of the disease-centric knowledge extraction tasks. In this paper, we present a machine learning based approach that uses a feature set tailored for disease mention recognition and outperforms the state-ofthe-art results. The paper also discusses why a feature set for the well studied gene/protein mention recognition task is not necessarily equally effective for other biomedical semantic types such as diseases.
For example, REF present a conditional random fields (CRFs) classifier using a feature set tailored for DER, achieving the best performance approaches on the Arizona Disease Corpus.
Disease Mention Recognition with Specific Features
Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Computer Science