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

Autor:	Christophe, Clément, Velcin, Julien, Cugliari, Jairo, Suignard, Philippe, Boumghar, Manel
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Information Retrieval Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 16 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1909.05099 Zobrazit plný text záznamu View this record from Arxiv