A methodology to enhance spatial understanding of disease outbreak events reported in news articles
A methodology to enhance spatial understanding of disease outbreak events reported in news articles
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DOI：
10.1016/j.ijmedinf.2010.01.014
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发表时间：
2010-04-01
影响因子：
4.9
通讯作者：
Collier, Nigel
中科院分区：
文献类型：
--
作者：
Chanlekha, Hutchatai;Collier, Nigel
目的：过去几年国际关注的疾病暴发的出现和再次出现，提高了利用开放媒体及时和准确地检测事件的卫生监测系统的重要性。然而，当前以事件为基础的健康监测系统面临的关键障碍之一是确定疫情地理位置的细粒度术语。在这篇文章中，我们提出了一种方法，通过将每个报道的事件与新闻报道中最具体的空间信息相关联来解决这个问题。这不仅适用于健康监测系统，也适用于其他以事件为中心的处理系统。方法：为了开发一个自动化的空间属性标注系统，我们首先创建了一个用于训练机器学习模型的黄金标准语料库。由于对数据的定性分析表明，事件类可能会对空间属性标注产生影响，我们还开发了一个事件分类系统，将事件类信息融入到空间属性标注模型中。为了自动识别事件的空间属性，探索了几种方法，从简单的启发式技术到基于最先进的条件随机场(CRF)模型的更复杂的方法。在模型中加入不同的特征集并进行比较。结果：对100篇疫情新闻进行了评估。基于查准率、查全率和查准率与查全率的调和平均数(F-Score)这三个指标对空间属性识别性能进行了评估。在本文提出的三种策略中，CRF模型在空间属性识别方面表现最好，F-Score的性能最好，达到85.5%(准确率86.3%，召回率84.7%)。结论：我们提出了一种在最精细的粒度水平上将媒体爆发报告中的每个事件与其空间属性相关联的方法。我们的目标是提供一种手段，加强对与疫情有关的事件的空间了解。评价研究表明，自动空间属性标注取得了良好的效果。在未来，我们计划探索更多的特征，例如词之间的语义相关性，这些特征可能对空间属性标注任务有用。(C)2010爱思唯尔爱尔兰有限公司。保留所有权利。
Purpose: The emergence and re-emergence of disease outbreaks of international concern in the last several years has raised the importance of health surveillance systems that exploit the open media for their timely and precise detection of events. However, one of the key barriers faced by current event-based health surveillance systems is in identifying fine-grained terms for an outbreak's geographical location. In this article, we present a method to tackle this problem by associating each reported event with the most specific spatial information available in a news report. This would be useful not only for health surveillance systems, but also for other event-centered processing systems.Methods: To develop an automated spatial attribute annotation system, we first created a gold standard corpus for training a machine learning model. Since the qualitative analysis on data suggested that the event class might have an impact on the spatial attribute annotation, we also developed an event classification system to incorporate event class information into the spatial attribute annotation model. To automatically recognize the spatial attribute of events, several approaches, ranging from a simple heuristic technique to a more sophisticated approach based on a state-of-the-art Conditional Random Fields (CRFs) model were explored. Different feature sets were incorporated into the model and compared.Results: The evaluations were conducted on 100 outbreak news articles. Spatial attribute recognition performance was evaluated based on three metrics; precision, recall and the harmonic mean of precision and recall (F-score). Among three strategies proposed in this article, the CRF model appeared to be the most promising for spatial attribute recognition with a best performance of 85.5% F-score (86.3% precision and 84.7% recall).Conclusion: We presented a methodology for associating each event in media outbreak reports with their spatial attribute at the finest level of granularity. Our goal has been to provide a means for enhancing the spatial understanding of outbreak-related events. Evaluation studies showed promising results for automatic spatial attribute annotation. In the future, we plan to explore more features, such as semantic correlation between words, that maybe useful for the spatial attribute annotation task. (C) 2010 Elsevier Ireland Ltd. All rights reserved.