基于深度学习的中医古文献临床经验抽取

中医古文献蕴藏着丰富的临床经验，是古代中医在行医过程中对临床诊疗的经验性总结，体现了中医学形成和发展的理论框架和思想基础.然而这些宝贵的临床经验不仅量大，而且分散在不同的文献中，使得中医从业者手工很难快速全面地获取它们，文献检索工具也只能提供文档级别的信息筛选，无法为这种细粒度的信息获取提供支持.此外，古汉语相对于现代汉语的不同特点也限制了主流文本分析工具的使用效果.为此本文提出面向临床经验获取的中医古文献信息抽取任务，用于识别古文献中描述临床经验的文本片段，手工标注了样本数据用于这种抽取模型的训练和测试，并设计了基于深度学习的序列标注器用于完成该任务.考虑到标注数据量小可能带来的过度拟合问题，本文引入对抗训练和虚拟对抗训练来增强模型的泛化能力.一系列充分的实验验证了模型的有效性，表明利用信息抽取技术从古文献获取中医临床经验具有可行性，为这一新的信息抽取任务提供了有希望的研究基线和可复用的标注数据集.

Extracting clinical experiences from ancient literature of Traditional Chinese Medicine via deep learning

Ancient literature of Traditional Chinese Medicine (TCM) contains rich clinical experiences, which is the empirical summary of clinical diagnosis and treatment in the process of ancient Chinese medicine practice, and embodies the theoretical framework and ideological basis of the formation and development of TCM. However, due to the volume and dispersion of valuable clinical experiences, it is difficult for TCM doctors to quickly and comprehensively obtain the clinical information they need from ancient literature manually, and the document retrieval tools can only provide document level information screening, which cannot support fine grained information extraction. In addition, the different characteristics of ancient Chinese relative to modern Chinese also limit the use of mainstream text analysis tools. For this reason, we propose a task of information extraction from the ancient literature of TCM for obtaining clinical experiences, which is used to identify text fragments describing clinical experiences in ancient literature and manually annotate sample data for training and testing the extraction task, a sequence labeling model is designed based on deep learning to complete the task. Considering the overfitting problem that can be brought about by the small amount of annotated data, we introduce adversarial training and virtual adversarial training to enhance the generalization ability of the proposed model. A series of sufficient experiments are conducted on the clinical experience dataset to verify the effectiveness of the model, and the experimental results show the feasibility of extracting clinical experiences from ancient literature by information extraction technology, and a promising baseline and a reusable annotated dataset for the new information extraction task are available.