如何检测文本数据流中的新颖性？现有方法的比较研究
How to detect novelty in textual data streams? A comparative study of existing methods
摘要：
由于在文档和/或单词级别的新颖性注释的数据集不容易获得，我们提出了一个模拟框架，允许我们创建不同的文本数据集，我们控制新奇的方式发生。我们还提出了一个基准的文本数据流中的新颖性检测的现有方法。我们定义了几个任务来解决和比较几个国家的最先进的方法。仿真框架允许我们根据一组有限的场景来评估它们的性能，并测试它们对某些参数的敏感性。最后，我们在《纽约时报》注释数据集中对不同类型的新奇事物进行了相同的实验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
