文章摘要
杜圣梅 朱礼军 徐 硕.面向循证医学的科技文献摘要结构化表示研究[J].中国科技资源导刊,2018,(6):94~100
面向循证医学的科技文献摘要结构化表示研究
Research on Structured Presentation of Scientific Literature Abstracts for Evidence-based Medicine
投稿时间:2018-06-06  
DOI:
中文关键词: 循证医学;SVM;句子分类;知识挖掘;机器学习
英文关键词: evidence based medicine, SVM, sentence classification, knowledge mining, machine learning
基金项目:北京市社会科学基金项目“大数据驱动的可制造性知识挖掘与管理方法研究”(17GLB074);北京市优秀人才培养资 助青年骨干个人项目”(2015000020124G052)。
作者单位
杜圣梅 朱礼军 徐 硕 (1. 中国科学技术信息研究所,北京 100038;2. 北京工业大学,北京 100124) 
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中文摘要:
      临床科学研究往往以科技文献的形式储存。文章对医学领域科技文献表示模型进行概述和分析,以PIBOSO 模型为基础,采用支持向量机对科技文献的摘要句子进行分类,实现了科技文献摘要信息的自动化抽取及关键句子的 识别,从而将科技文献的摘要内容进行语义关系的量化和结构化表示,为临床医师和相关研究人员在寻找证据资源时 提供有效借鉴和帮助。实验结果表明,该方法的F值在大多数类别上高于其他方法,表明研究方案具有可行性和有效 性。
英文摘要:
      It is well known that clinical scientific research is often stored in scientific and technical (S&T) literature. The knowledge hidden in S&T literature can provide clinicians and researchers with the clinical decision-making evidence in the practice of evidence-based medicine. After the representation models for S&T literature are summarized and analyzed in the medical field, are fined PIBOSO model is used in this study. For purpose of the automatic extraction of the abstract and the identification of key sentences, Support Vector Machine (SVM) is utilized here to classify abstract sentences. The classification results with SVM help quantify the semantic relations and structure the abstracts, thus providing effective reference for clinicians and researchers to find evidence. From experimental results, one can see that F-score in this work is higher than the counterparts in most categories, which indicates that our research framework is feasible and effective in the sentence classification task from the biomedical field.
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