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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 4
1059-1062

Temporal phrases extraction in clinical text based on bidirectional long-short term memory model

Zhang Shunli1
Wang Yingjun1
Ji Donghong2
1. School of Information Engineering, Henan Institute of Science & Technology, Xinxiang Henan 453003, China
2. National Network Security College, Wuhan University, Wuhan 430205, China
DOI: 10.19734/j.issn.1001-3695.2018.09.0742

Abstract

Recognizing time phrases from clinical text is a fundamental task for many applications in clinical NLP. Traditional methods based on rules and machine learning require the design of complex rules and feature extraction, and the serial method used by most systems may lead to error propagation. This paper proposed a novel neural network based on bidirectional long-short term memory(BLSTM) to identifying clinical time expressions and the type of them simultaneously. Firstly, it combined character-level word embedding trained by convolutional neural network(CNN) with word embedding trained from large-scale biomedical corpus together as input to BLSTM. Then it utilized BLSTM to model context information of each word. Finally, it employed conditional random field(CRF) to optimize the output of BLSTM. This paper evaluated the model task 12 of on the Semeval-2016. It receives the best F1 value without requiring any handcrafted features or rules. Compared with the state-of-the-art systems in this task, the proposed model improves the F1 scores by 3%.

Foundation Support

国家自然科学基金资助项目(61373108)
河南省重点科研研究项目(15A520069)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.09.0742
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 4
Section: Algorithm Research & Explore
Pages: 1059-1062
Serial Number: 1001-3695(2020)04-020-1059-04

Publish History

[2020-04-05] Printed Article

Cite This Article

张顺利, 王应军, 姬东鸿. 基于BLSTM网络的医学时间短语识别 [J]. 计算机应用研究, 2020, 37 (4): 1059-1062. (Zhang Shunli, Wang Yingjun, Ji Donghong. Temporal phrases extraction in clinical text based on bidirectional long-short term memory model [J]. Application Research of Computers, 2020, 37 (4): 1059-1062. )

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.

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