[1]赵君珂,张振宇,蔡开裕.基于自然语言处理的医学实体识别与标签提取[J].计算机技术与发展,2019,29(09):18-23.[doi:10. 3969 / j. issn. 1673-629X. 2019. 09. 004]
　ZHAO Jun-ke,ZHANG Zhen-yu,CAI Kai-yu.Medical Entity Recognition and Label Extraction Based on Natural Language Processing[J].,2019,29(09):18-23.[doi:10. 3969 / j. issn. 1673-629X. 2019. 09. 004]
点击复制

基于自然语言处理的医学实体识别与标签提取()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年09期
页码:
18-23
栏目:
智能、算法、系统工程
出版日期:
2019-09-10

文章信息/Info

Title:
Medical Entity Recognition and Label Extraction Based on Natural Language Processing
文章编号:
1673-629X(2019)09-0018-06
作者:
赵君珂; 张振宇; 蔡开裕
国防科技大学,湖南 长沙 410073
Author(s):
ZHAO Jun-ke; ZHANG Zhen-yu; CAI Kai-yu
National University of Defense Technology,Changsha 410073,China
关键词:
自然语言处理; 医学数据; 非结构化; 实体识别; 标签提取
Keywords:
natural language processing; medical data; unstructured; entity identification; label extraction
分类号:
TP31
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 09. 004
摘要:
随着信息化建设的快速发展,数据产生了爆炸式的增长,医院每天也同样产生大量的医疗记录与数据。 其中大部分内容是非结构化数据,具有真实性、主观性和不规范性,不利于解读和处理。 由于医疗数据是以非结构化的文本形式存储的,因此无法直接通过计算机直接处理和分析,不仅效率低下,分析质量也无法保证。 目前的信息抽取研究中使用的方法的可扩展性都较差,具有一些局限性,故自动化程度不高。 文中通过自然语言处理中的规则描述语言方法,对数据中非结构化的医学命名实体进行识别,并通过语义分析进行标签提取,使非结构化的数据结构化,让数据中的描述更为准确、统一。 优化了目前信息抽取方法中存在的可扩展性差的缺点,能够根据情况适应不同的情景。
Abstract:
With the rapid development of information construction,data has exploded. Hospitals also produce a large number of medical records and data every day. Most of them are unstructured data with authenticity,subjectivity and irregularity,which is not conducive to interpretation and processing. Since medical data is stored in the form of unstructured text,it cannot be directly processed and analyzed by computer,which is not only inefficient,but also cannot guarantee the quality of analysis. At present,the methods used in information extraction research have poor scalability and some limitations,so the degree of automation is not high. We recognize unstructured medical named entities in data by rule description language method in natural language processing,and extract labels by semantic analysis,so that unstructured data can be structured to make the description of data more accurate and unified. It also optimizes the shortcomings of poor scalability in current information extraction methods,and can adapt to different scenarios according to the situation.

相似文献/References:

[1]陈国华 赵克 李亚涛 易帅.自然语言处理系统中的事件类名词的耦合处理[J].计算机技术与发展,2008,(06):60.
　CHEN Guo-hua,ZHAO Ke,LI Ya-tao,et al.Coupling Processing of Event Noun in NLP Systems[J].,2008,(09):60.
[2]程节华.基于FAQ的智能答疑系统中分词模块的设计[J].计算机技术与发展,2008,(07):181.
　CHENG Jie-hua.Design of Words Module in Intelligent Q/A System Based on FAQ[J].,2008,(09):181.
[3]杨欢 许威 赵克 陈余.动词属性在自然语言处理当中的研究与应用[J].计算机技术与发展,2008,(07):233.
　YANG Huan,XU Wei,ZHAO Ke,et al.Research and Application of Verb Attributes in Natural Language Processing[J].,2008,(09):233.
[4]孙超 张仰森.面向综合语言知识库的知识融合与获取研究[J].计算机技术与发展,2010,(08):25.
　SUN Chao,ZHANG Yang-sen.Research of Knowledge Integration and Obtaining Oriented Comprehensive Language Knowledge System[J].,2010,(09):25.
[5]党建 亿珍珍 赵克 殷鸿.数学领域集体词结构形式化处理研究[J].计算机技术与发展,2007,(05):121.
　DANG Jian,YI Zhen-zhen,ZHAO Ke,et al.Research of Formalization Processing for Collective Structures in Mathematics Domain[J].,2007,(09):121.
[6]江有福 郑庆华.自然语言网络答疑系统中倒排索引技术的研究[J].计算机技术与发展,2006,(02):126.
　JIANG You-fu,ZHENG Qing-hua.Research of Inverted Index in NLWAS[J].,2006,(09):126.
[7]刘亚清 张瑾 于纯妍.基于义原同现频率的汉语词义排歧系统[J].计算机技术与发展,2006,(05):184.
　LIU Ya-qing,ZHANG Jin,YU Chun-yan.A Chinese Word Sense Disambiguation System Based on Primitive CO- Occurrence Data[J].,2006,(09):184.
[8]刘政怡 李炜 吴建国.基于IMM—IME的汉字键盘输入法编程技术研究[J].计算机技术与发展,2006,(12):43.
　LIU Zheng-yi,LI Wei,WU Jian-guo.Research of Programming Technology of Chinese Input Method Based on IMM- IME[J].,2006,(09):43.
[9]赵鹏 何留进 孙凯 方薇[].基于情感计算的网络中文信息分析技术[J].计算机技术与发展,2010,(11):146.
　ZHAO Peng,HE Liu-jin,SUN Kai,et al.Analyzing Technologies of Internet Chinese Information Based on Affective Computing[J].,2010,(09):146.
[10]徐远方 李成城.基于SVM和词间特征的新词识别研究[J].计算机技术与发展,2012,(05):134.
　XU Yuan-fang,LI Cheng-cheng.Research on New Word Identification Based on SVM and Word Characteristics[J].,2012,(09):134.

更新日期/Last Update: 2019-09-10