Two-Phase Biomedical Named Entity Recognition Using A Hybrid Method

Biomedical named entity recognition (NER) is a difficult problem in biomedical information processing due to the widespread ambiguity of terms out of context and extensive lexical variations. This paper 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

• Type: Conference
• Year: 2005
• Where: IJCNLP
• Authors: Seonho Kim, Juntae Yoon, Kyung-Mi Park, Hae-Chang Rim
• Keywords: Biomedical Information Processing, Biomedical Named Entity, IJCNLP 2005, Natural Language Processing, Two-phase Biomedical Ner