Research on the Method and System of Word Segmentation and POS Tagging for Ancient Chinese Medicine Literature Objectives: To better explore the valuable experience and knowledge in traditional Chinese medicine (TCM) literature and promote the academic heritage and innovation of TCM, this paper tries to provide methods and a system of word segmentation and POS tagging for ancient Chinese medicine literature. Method: Based on HMM and Java language for POS tagging, this paper develops a word segmentation system of Chinese ancient books by constructing a Thesaurus of Chinese medicine terminology and a special POS tagging method for Chinese medicine, and adopting Ansj open source code as the core word segmentation algorithm. Results: Thesaurus of Chinese medicine terminology involving 155,343 Chinese medicine words are constructed. These words were divided into 14 categories and 7 levels of 891 parts of speech (POS). Online website of Chinese word segmentation and POS tagging proprietary system were established (http:www.zhongyifenci.org). And the F measure reaches 90.65%, which is much higher than the system of Ansj. Clinical or Biological Impact: This system, with high precision and recall rate, will be very helpful to knowledge discovery of Chinese medicine and be beneficial to giving full play to the original advantages of Chinese medicine.

TCM Translator: A Sequence Generation Approach for Prescribing Herbal Medicines In traditional Chinese medicine (TCM), herbal prescriptions are accumulated from doctorsu0027 clinical experience and play an essential role in treatment process for more than thousands of years. Mining their containing manytomany relationship between symptoms and herbs are important for both clinical practice and novel prescriptions development. Previously, the research attention in this field is attracted by topic models and multilabel classification approaches, but they donu0027t fully capture the correlations of labels and achieve very promising performance. Alternatively, we problematic it as a machine translation problem, that the source sentence is formed by symptoms and the target sentence consists of herbs. Two sentences are both in weakly order. In terms of this assumption, we propose a novel sequencetosequence (seq2seq) architecture, namely TCM translator, to translate symptoms to herbs. Seq2seq, consisting of an encoder and a decoder, is a well known framework for resolving the machine translation problem in natural language processing (NLP) community. It maps a bag of symptoms to the latent space, which is decoded to the target sentence, i.e., herbs. In this paper, transformer is used as our encoder to explore the latent correlations of symptoms and LSTM is implemented as the decoder to generate the herbs sequentially. The experimental results demonstrate that our proposed approach is effective and outperforms other baseline models by a substantial margin.