In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 12
3744-3747

TCM text relationship extraction model based on bidirectional LSTM and GBDT

Luo Jigen
Du Jianqiang
Nie Bin
Xiong Wangping
Liu Lei
He Jia
School of Computer, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China
DOI: 10.19734/j.issn.1001-3695.2018.07.0420

Abstract

In order to solve the problem that the use of softmax as a long-short-term memory network classifier leads to the lack of generalization ability of the entity relationship recognition model, it is not suitable for the extraction of TCM entity relationships. This paper proposed a bidirectional long short-term memory(BILSTM) relational identification algorithm(BILSTM-GBDT) that incorporates a gradient boosting decision tree(GBDT). Firstly, it trained the Chinese medicine text vector by word2vec, then extracted the high-order features by the bidirectional long short-term memory network based on the attention mechanism. Finally, it used the integrated classification model gradient lifting tree as the feature classifier to improve the relationship recognition effect. Experimental results on multiple relational corpora such as Chinese medicine show that the model has higher accuracy, recall and F value than traditional SVM method, GBDT method and deep learning method.

Foundation Support

国家自然科学基金资助项目(61363042,61562045,61762051)
江西省科技厅重大研发计划资助项目(20171ACE50021)
江西省研究生创新专项资金资助项目(YC2017-S349)
江西省科技厅重点研发计划资助项目(20171BBG70108)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.07.0420
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 12
Section: Algorithm Research & Explore
Pages: 3744-3747
Serial Number: 1001-3695(2019)12-047-3744-04

Publish History

[2019-12-05] Printed Article

Cite This Article

罗计根, 杜建强, 聂斌, 等. 基于双向LSTM和GBDT的中医文本关系抽取模型 [J]. 计算机应用研究, 2019, 36 (12): 3744-3747. (Luo Jigen, Du Jianqiang, Nie Bin, et al. TCM text relationship extraction model based on bidirectional LSTM and GBDT [J]. Application Research of Computers, 2019, 36 (12): 3744-3747. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)