Context-based Bidirectional-LSTM Model for Sequence Labeling in Clinical Reports
Proceedings of SPIE(2019)
摘要
Recurrent Neural Network (RNN) models have been widely used for sequence labeling applications in different domains. This paper presents an RNN-based sequence labeling approach with the ability to learn long-term labeling dependencies. The proposed model has been successfully used for a Named Entity Recognition challenge in healthcare: anatomical phrase labeling in radiology reports. The model was trained on labeled data from a radiology report corpus and tested on two independent datasets. The proposed model achieved promising performance in comparison with other state-of-the-art context-driven sequence labeling approaches.
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关键词
Named Entity Recognition,Sequence Labeling,Nature Language Processing
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