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

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