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Erschienen in:
2020 | OriginalPaper | Buchkapitel
How to Detect Novelty in Textual Data Streams? A Comparative Study of Existing Methods
verfasst von : Clément Christophe, Julien Velcin, Jairo Cugliari, Philippe Suignard, Manel Boumghar
Erschienen in: Advanced Analytics and Learning on Temporal Data
Abstract
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.