标题： 如何检测文本数据流中的新颖性？ 现有方法的比较研究 显示英文标题

标题： How to detect novelty in textual data streams? A comparative study of existing methods

摘要： 由于不容易获得带有文档和/或词级新颖性标注的数据集，我们提出了一种仿真框架，使我们可以创建不同的文本数据集，在其中控制新颖性的发生方式。 我们还展示了现有方法在文本数据流中的新颖性检测基准。 我们定义了一些任务来解决，并比较了几种最先进的方法。 仿真框架允许我们根据一组有限的场景评估它们的性能，并测试它们对某些参数的敏感性。 最后，我们在《纽约时报》注释数据集的不同新颖性上使用相同的方法进行了实验。 显示英文摘要

摘要： Since datasets with annotation for novelty at the document and/or word level are not easily available, we present a simulation framework that allows us to create different textual datasets in which we control the way novelty occurs. We also present a benchmark of existing methods for novelty detection in textual data streams. We define a few tasks to solve and compare several state-of-the-art methods. The simulation framework allows us to evaluate their performances according to a set of limited scenarios and test their sensitivity to some parameters. Finally, we experiment with the same methods on different kinds of novelty in the New York Times Annotated Dataset.