Serialized Co-Training-Based Recognition of Medicine Names for Patent Mining and Retrieval

Author:

Deng Na1,Xiong Caiquan1

Affiliation:

1. Hubei University of Technology, China

Abstract

In the retrieval and mining of traditional Chinese medicine (TCM) patents, a key step is Chinese word segmentation and named entity recognition. However, the alias phenomenon of traditional Chinese medicines causes great challenges to Chinese word segmentation and named entity recognition in TCM patents, which directly affects the effect of patent mining. Because of the lack of a comprehensive Chinese herbal medicine name thesaurus, traditional thesaurus-based Chinese word segmentation and named entity recognition are not suitable for medicine identification in TCM patents. In view of the present situation, using the language characteristics and structural characteristics of TCM patent texts, a modified and serialized co-training method to recognize medicine names from TCM patent abstract texts is proposed. Experiments show that this method can maintain high accuracy under relatively low time complexity. In addition, this method can also be expanded to the recognition of other named entities in TCM patents, such as disease names, preparation methods, and so on.

Publisher

IGI Global

Subject

Hardware and Architecture,Software

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021;Evidence-Based Complementary and Alternative Medicine;2022-05-13

2. Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM-CRF;Wireless Communications and Mobile Computing;2021-06-02

3. Literature Listing;World Patent Information;2020-12

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