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Categorizing Emails Using Machine Learning with Textual Features Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Weaving Information Propagation: Modeling the Way Information Spreads in Document Collections

Authors : Charles Huyghues-Despointes, Leila Khouas, Julien Velcin, Sabine Loudcher

Published in: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

Information usually spreads between people by the mean of textual documents. During such propagation, a piece of information can either remain the same or mutate. We propose to formulate information spread with a set of time-ordered document chains along which some information has likely been transmitted. This formulation is different from the usual graph view of a transmission process as it integrates a notion of lineage of the information. We also propose a way to construct a candidate set of document chains for the information propagation in a corpus of documents. We show that most of the chains have been judged as plausible by human experts.

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previous chapter Automatic Generation of Video Game Character Images Using Augmented Structure-and-Style Networks
next chapter Mitigating Overfitting Using Regularization to Defend Networks Against Adversarial Examples
Footnotes
1
The full dataset can be obtained at: https://​aminer.​org/​citation.
 
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Metadata
Title
Weaving Information Propagation: Modeling the Way Information Spreads in Document Collections
Authors
Charles Huyghues-Despointes
Leila Khouas
Julien Velcin
Sabine Loudcher
Copyright Year
2019
Book
Advances in Artificial Intelligence

Print ISBN: 978-3-030-18304-2

Electronic ISBN: 978-3-030-18305-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-18305-9_35

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