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论文类型：会议论文

发表时间：2015-11-09

收录刊物：EI、CPCI-S、Scopus

页面范围：649-652

关键字：bio-NER; recurrent neural network; hand-designed features; word embedding; context information

摘要：Biomedical named entity recognition (bio-NER), which extracts important entities such as genes and proteins, has become one of the most fundamental tasks in biomedical knowledge acquisition. However, the performance of traditional NER systems is always limited to the construction of complex hand-designed features which are derived from various linguistic analyses and maybe only adapted to specified area. In this paper we mainly focus on building a simple and efficient system for bio-NER with the extended Recurrent Neural Network (RNN) which considers the predicted information from the prior node and external context information (topical information & clustering information). Extracting complex hand-designed features is skipped and replaced with word embeddings. The experiments conducted on the BioCreative II GM data set demonstrate RNN models outperform CRF model and deep neural networks (DNN); furthermore, the extended RNN model performs better than the original RNN model.