Stratégies de sélection des exemples pour l’apprentissage actif avec des champs aléatoires conditionnels

Vincent Claveau 1 Ewa Kijak 1
1 LinkMedia - Creating and exploiting explicit links between multimedia fragments
IRISA-D6 - MEDIA ET INTERACTIONS, Inria Rennes – Bretagne Atlantique
Abstract : Nowadays, many NLP problems are modelized as supervised machine learning tasks. Consequently, the cost of the expertise needed to annotate the examples is a widespread issue. Active learning offers a framework to that issue, allowing to control the annotation cost while maximizing the classifier performance, but it relies on the key step of choosing which example will be proposed to the expert. In this paper, we examine and propose such selection strategies in the specific case of Conditional Random Fields (CRF) which are largely used in NLP. On the one hand, we propose a simple method to correct a bias of certain state-of-the-art selection techniques. On the other hand, we detail an original approach to select the examples, based on the respect of proportions in the datasets. These contributions are validated over a large range of experiments implying several tasks and datasets, including named entity recognition, chunking, phonetization, word sens disambiguation.
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https://hal.archives-ouvertes.fr/hal-01206847
Contributor : Vincent Claveau <>
Submitted on : Tuesday, September 29, 2015 - 4:31:07 PM
Last modification on : Friday, January 11, 2019 - 4:23:36 PM

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  • HAL Id : hal-01206847, version 1

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Vincent Claveau, Ewa Kijak. Stratégies de sélection des exemples pour l’apprentissage actif avec des champs aléatoires conditionnels. Conférence TALN 2015, Jun 2015, Caen, France. ⟨hal-01206847⟩

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