亚利桑那大学参加MADE1.0自然语言处理挑战赛。
UArizona at the MADE1.0 NLP Challenge.
作者信息
Xu Dongfang, Yadav Vikas, Bethard Steven
机构信息
School of Information, University of Arizona, USA.
出版信息
Proc Mach Learn Res. 2018 May;90:57-65.
Abstract
MADE1.0 is a public natural language processing challenge aiming to extract medication and adverse drug events from Electronic Health Records. This work presents NER and RI systems developed by UArizona team for the MADE1.0 competition. We propose a neural NER system for medical named entity recognition using both local and context features for each individual word and a simple but effective SVM-based pairwise relation classification system for identifying relations between medical entities and attributes. Our system achieves 81.56%, 83.18%, and 59.85% F1 score in the three tasks of MADE1.0 challenge, respectively, ranked amongst the top three teams for Task 2 and 3.
摘要
MADE1.0是一项公共自然语言处理挑战，旨在从电子健康记录中提取药物和药物不良事件。这项工作展示了亚利桑那大学团队为MADE1.0竞赛开发的命名实体识别（NER）和关系识别（RI）系统。我们提出了一种用于医学命名实体识别的神经NER系统，该系统使用每个单词的局部特征和上下文特征，以及一个简单但有效的基于支持向量机（SVM）的成对关系分类系统，用于识别医学实体与属性之间的关系。我们的系统在MADE1.0挑战的三项任务中分别取得了81.56%、83.18%和59.85%的F1分数，在任务2和任务3中排名前三。
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