Paper: Two-Phase Biomedical Named Entity Recognition Using A Hybrid Method

ACL ID I05-1057
Title Two-Phase Biomedical Named Entity Recognition Using A Hybrid Method
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2005
Authors

Biomedical named entity recognition (NER) is a difficult problem in biomedical information processing due to the widespread am- biguity of terms out of context and extensive lexical variations. This pa- per presents a two-phase biomedical NER consisting of term boundary detection and semantic labeling. By dividing the problem, we can adopt an effective model for each process. In our study, we use two exponential models, conditional random fields and maximum entropy, at each phase. Moreover, results by this machine learning based model are refined by rule-based postprocessing implemented using a finite state method. Ex- periments show it achieves the performance of F-score 71.19% on the JNLPBA 2004 shared task of identifying 5 classes of biomedical NEs.