A Maximum Entropy Approach to Biomedical Named Entity Recognition

Machine learning approaches are frequently used to solve name entity (NE) recognition (NER). In this paper we propose a hybrid method that uses maximum entropy (ME) as the underlying machine learning method incorporated with dictionary-based and rule-based methods for post-processing. Simply using ME for NER, inaccurate boundary detection of NEs and misclassification may occur. Some NEs are partially recognized by ME. In the post-processing stage, we use dictionary-based and rule-based methods to extend boundary of partially recognized NEs and to adjust classification. We use GENIA corpus 3.01 to conduct 10-fold crossverification experiments. To evaluate the performance, we consider the longest NE annotations. We evaluate our approach using standard precision (P), recall (R), and F-score, where F-score is defined as 2PR/(P+R). The precision, recall and F-score ([P, R, F]) of our ME module for overall 23 categories is [0.512, 0.538, 0.525], and after the postprocessing the performance ...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Yi-Feng Lin, Tzong-Han Tsai, Wen-Chi Chou, Kuen-Pin Wu, Ting-Yi Sung, Wen-Lian Hsu
• Keywords: Data Mining, KDD 2004, MEbased NER Module, Post-processing Stage, Rule-based Methods