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UWM: A Simple Baseline Method for Identifying Attributes of Disease and Disorder Mentions in Clinical Text

机译:UWM:一种简单的基线方法,用于识别临床文本中的疾病和障碍因素

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In this paper the system that was developed by Team UWM for the Task 14 of SemEval 2015 competition is described. Task 14 included two tasks: Task 1 was identification of disorder mentions and their normalization, and Task 2 was identification of the following attributes for disorder mentions: the CUI of the disorder, negation indicator, subject, uncertainty indicator, course, severity, conditional, generic indicator, and body location. For Task 1, an earlier system was applied that uses Conditional Random Fields (CRFs) for disorder recognition and learned edit distance patterns for normalization. Task 2 was implemented by a simple method that finds the attribute terms around the disease mentions by matching them in the training data. Among all participants Team UWM was ranked fourth in Task 1, fourth in Task 2A (over gold-standard mentions) and third in Task 2B (over extracted mentions).
机译:本文介绍了由UWM团队针对SemEval 2015竞赛的Task 14开发的系统。任务14包括两个任务:任务1是识别疾病提及及其规范化,任务2识别疾病提及的以下属性:疾病的CUI,否定指标,受试者,不确定性指标,病程,严重程度,条件,通用指标和身体位置。对于任务1,应用了较早的系统,该系统使用条件随机场(CRF)进行障碍识别,并使用学习的编辑距离模式进行归一化。任务2是通过一种简单的方法实施的,该方法通过在训练数据中进行匹配来找到疾病提及周围的属性项。在所有参与者中,UWM团队在任务1中排名第四,在任务2A中排名第四(超过金标准的提及),在任务2B中排名第三(超过摘录的提及)。

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