用于自动生物监视事件检测的新闻警报的多文档表示
Multiple Document Representations from News Alerts for Automated Bio-surveillance Event Detection
摘要：
由于全球化，地理边界不再是传染病传播的有效屏障。为了帮助生物监测分析人员进行疾病跟踪，最近的研究致力于开发利用因特网上大量公开可用文档的信息检索和分析方法。在这项工作中，我们提出的方法，自动检索和分类的文件相关的积极的公共卫生事件。我们展示了自动生成的语料库上的分类性能，使用递归神经网络，TF-IDF和朴素贝叶斯对数计数比文档表示。通过对文档的标题和描述进行联合建模，我们使用性能最好的生物监测事件分类模型实现了97%的召回率和93.3%的准确率：对一对双向递归神经网络的组合输出进行逻辑回归。
英文摘要：
Due to globalization, geographic boundaries no longer serve as effective shields for the spread of infectious diseases. In order to aid bio-surveillance analysts in disease tracking, recent research has been devoted to developing information retrieval and analysis methods utilizing the vast corpora of publicly available documents on the internet. In this work, we present methods for the automated retrieval and classification of documents related to active public health events. We demonstrate classification performance on an auto-generated corpus, using recurrent neural network, TF-IDF, and Naive Bayes log count ratio document representations. By jointly modeling the title and description of a document, we achieve 97% recall and 93.3% accuracy with our best performing bio-surveillance event classification model: logistic regression on the combined output from a pair of bidirectional recurrent neural networks.
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