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Robust Functional Regression for Outlier Detection Read chapter Read first chapter

2020 | OriginalPaper | Chapter

How to Detect Novelty in Textual Data Streams? A Comparative Study of Existing Methods

Authors : Clément Christophe, Julien Velcin, Jairo Cugliari, Philippe Suignard, Manel Boumghar

Published in: Advanced Analytics and Learning on Temporal Data

Publisher: Springer International Publishing

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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.

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Footnotes
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Metadata
Title
How to Detect Novelty in Textual Data Streams? A Comparative Study of Existing Methods
Authors
Clément Christophe
Julien Velcin
Jairo Cugliari
Philippe Suignard
Manel Boumghar
Copyright Year
2020
Book
Advanced Analytics and Learning on Temporal Data

Print ISBN: 978-3-030-39097-6

Electronic ISBN: 978-3-030-39098-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39098-3_9

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