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.

As a common biological sequence, DNA sequences contain important information. The discovery of frequent patterns in DNA sequences can help to study the evolution, function and variation of genes. The findings are of great significance to genetic and mutation analysis, analysis of disease causes and treatment of diseases. Traditional methods of frequent pattern discovery need to scan DNA sequences multiple times. To overcome this shortcoming, this article proposes a new method to discover frequent patterns from DNA sequences. This method is based on a two-level nested hash table data structure and set operation. All frequent patterns and their positions in DNA sequences can be found by scanning DNA sequences only once. Experimental results show that this method can correctly recognize all the frequent patterns in DNA sequences and their locations. The method can also be applied to discover frequent patterns in RNA, protein or other biological sequences.