Towards role-based filtering of disease outbreak reports
Towards role-based filtering of disease outbreak reports
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DOI：
10.1016/j.jbi.2008.12.009
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发表时间：
2009-10-01
影响因子：
4.5
通讯作者：
Collier, Nigel
中科院分区：
文献类型：
--
作者：
Doan, Son;Kawazoe, Ai;Collier, Nigel
本文探讨了命名实体在疾病暴发报告分类中的作用。在公共卫生保护文本挖掘系统BioCaster的标注模式中，使用形式化的本体方法对反映传染病信息的重要概念进行了概念分析，并对其进行了类型和角色划分。类型被指定为NE类别，角色被整合到NE中，作为诸如化学物质的属性，以及它是否被用作某些传染病的治疗。我们将重点放在网元的角色上，并探索不同的方法来提取、组合和使用它们作为文本分类器的特征。此外，我们还考察了疾病相关名词和动词的角色与语义类别的结合。使用朴素贝叶斯和支持向量机算法的实验结果表明：(1)角色和NE相结合提高了文本分类的性能；(2)角色与名词和动词特征的语义类别相结合对文本分类的改善有很大贡献。与基线的“原始文本”表述相比，这两个结果在统计上都有显著意义。我们详细讨论了角色在文本分类任务的准确率、查准率/召回率和F-Score度量方面对每个NE以及名词和动词特征的语义类别的影响。(C)2008 Elsevier Inc.保留所有权利。
This paper explores the role of named entities (NEs) in the classification of disease outbreak report. In the annotation schema of BioCaster, a text mining system for public health protection, important concepts that reflect information about infectious diseases were conceptually analyzed with a formal ontological methodology and classified into types and roles. Types are specified as NE classes and roles are integrated into NEs as attributes such as a chemical and whether it is being used as a therapy for some infectious disease. We focus on the roles of NEs and explore different ways to extract, combine and use them as features in a text classifier. In addition, we investigate the combination of roles with semantic categories of disease-related nouns and verbs. Experimental results using naive Bayes and Support Vector Machine (SVM) algorithms show that: (1) roles in combination with NEs improve performance in text classification, (2) roles in combination with semantic categories of noun and verb features contribute substantially to the improvement of text classification. Both these results were statistically significant compared to the baseline "raw text" representation. We discuss in detail the effects of roles on each NE and on semantic categories of noun and verb features in terms of accuracy, precision/recall and F-score measures for the text classification task. (C) 2008 Elsevier Inc. All rights reserved.