Timely analysis of cyber-security information necessitates automated
information extraction from unstructured text. While state-of-the-art
extraction methods produce extremely accurate results, they require ample
training data, which is generally unavailable for specialized applications,
such as detecting security related entities; moreover, manual annotation of
corpora is very costly and often not a viable solution. In response, we develop
a very precise method to automatically label text from several data sources by
leveraging related, domain-specific, structured data and provide public access
to a corpus annotated with cyber-security entities. Next, we implement a
Maximum Entropy Model trained with the average perceptron on a portion of our
corpus ($\sim$750,000 words) and achieve near perfect precision, recall, and
accuracy, with training times under 17 seconds.

本文介绍了一种利用结构化数据来自动标注文本的精确方法，并提供一个标注了网络安全实体的语料库。我们使用平均感知器上的最大熵模型对我们的语料库进行训练，训练时间不到 17 秒，并实现了接近完美的精确度、召回率和准确性。