With the growing popularity of traditional Chinese medicine (TCM) in the world and the increasing awareness of intellectual property protection, the number of TCM patent application is growing year by year. TCM patents contain rich medical, legal, and economic information. Effective text mining of TCM patents is of great theoretical and practical significance (e.g., the R&D of new medicines, patent infringement litigation, and patent acquisition). Named entity recognition (NER) is a fundamental task in natural language processing and a crucial step before indepth analysis of TCM patent. In this paper, a method combining Bidirectional Long Short-Term Memory neural network with Conditional Random Field (BiLSTM-CRF) is proposed to automatically recognize entities of interest (i.e., herb names, disease names, symptoms, and therapeutic effects) from the abstract texts of TCM patents. By virtue of the capabilities of deep learning methods, the semantic information in the context can be learned without feature engineering. Experiments show that the BiLSTM-CRF-based method provides superior performance in comparison with various baseline methods.

中文翻译：

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基于BiLSTM-CRF的中药专利命名实体识别

随着中医药在世界范围内的日益普及和知识产权保护意识的增强，中医药专利申请量逐年增加。中医专利包含丰富的医学、法律和经济信息。有效的中药专利文本挖掘具有重要的理论和实践意义（如新药研发、专利侵权诉讼、专利获取等）。命名实体识别（NER）是自然语言处理中的一项基本任务，也是深入分析中医专利之前的关键步骤。在本文中，提出了一种将双向长短期记忆神经网络与条件随机场（BiLSTM-CRF）相结合的方法来自动识别感兴趣的实体（即草药名称、疾病名称、症状、和治疗效果）来自中医专利的摘要文本。借助深度学习方法的能力，无需特征工程即可学习上下文中的语义信息。实验表明，与各种基线方法相比，基于 BiLSTM-CRF 的方法提供了更优越的性能。