Named Entity Recognition in Chinese Electronic Medical Records Based on CRF

2017 14th Web Information Systems and Applications Conference (WISA) Pub Date : 2017-11-01 DOI:10.1109/WISA.2017.8

Kaixin Liu, Qingcheng Hu, Jianwei Liu, Chunxiao Xing

引用次数: 24

Abstract

Massive Electronic Medical Records (EMRs) contain a lot of knowledge and Named Entity Recognition (NER) in Chinese EMR is a very important task. However, due to the lack of Chinese medical dictionary, there are few studies on NER in Chinese EMR. In this paper, we first build a medical dictionary. We then investigated the effects of different types of features in Chinese clinical NER tasks based on Condition Random Fields (CRF) algorithm, the most popular algorithm for NER, including bag-of-characters, part of speech, dictionary feature, and word clustering features. In the experimental section, we randomly selected 220 clinical texts from Peking Anzhen Hospital. The experimental results showed that these features were beneficial in varying degrees to Chinese named entity recognition. Finally, after analyzing the experimental results, we get some rules of thumb.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于CRF的中文电子病历命名实体识别

海量电子病历包含了大量的知识，中文电子病历的命名实体识别是一项非常重要的任务。然而，由于缺乏中文医学词典，对中文电子病历中NER的研究很少。本文首先构建了一个医学词典。基于条件随机场(CRF)算法，包括字符袋特征、词性特征、字典特征和聚类特征，研究了不同类型特征对汉语临床NER任务的影响。在实验部分，我们随机抽取北京安贞医院的220篇临床文献。实验结果表明，这些特征对中文命名实体识别都有不同程度的帮助。最后，通过对实验结果的分析，得出了一些经验法则。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

2017 14th Web Information Systems and Applications Conference (WISA)

自引率

0.00%

发文量

期刊最新文献

Efficient Time Series Classification via Sparse Linear Combination Checking the Statutes in Chinese Judgment Document Based on Editing Distance Algorithm Information Extraction from Chinese Judgment Documents Topic Classification Based on Improved Word Embedding Keyword Extraction for Social Media Short Text