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Strategies to select examples for Active Learning with Conditional Random Fields

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 tackled 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 some 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 datasets and tasks, including named entity recognition, chunking, phonetization, word sense disambiguation.
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Contributor : Vincent Claveau <>
Submitted on : Monday, October 23, 2017 - 11:44:07 AM
Last modification on : Friday, October 23, 2020 - 4:41:57 PM
Long-term archiving on: : Wednesday, January 24, 2018 - 1:40:20 PM


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  • HAL Id : hal-01621338, version 1


Vincent Claveau, Ewa Kijak. Strategies to select examples for Active Learning with Conditional Random Fields. CICLing 2017 - 18th International Conference on Computational Linguistics and Intelligent Text Processing, Apr 2017, Budapest, Hungary. pp.1-14. ⟨hal-01621338⟩



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