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A refined parsing graph approach to learn smaller contextually substitutable grammars with less data

François Coste 1 Mikail Demirdelen 1
1 Dyliss - Dynamics, Logics and Inference for biological Systems and Sequences
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : We present a refined parsing graph approach to learn smaller contextually substitutable grammars from smaller training samples in the framework initiated by ReGLiS algorithm.
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https://hal.inria.fr/hal-01406337
Contributor : François Coste <>
Submitted on : Thursday, December 1, 2016 - 9:47:54 AM
Last modification on : Friday, July 10, 2020 - 4:12:06 PM
Long-term archiving on: : Monday, March 20, 2017 - 4:48:23 PM

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  • HAL Id : hal-01406337, version 1

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François Coste, Mikail Demirdelen. A refined parsing graph approach to learn smaller contextually substitutable grammars with less data. ICGI 2016 - 13th International Conference on Grammatical Inference, Oct 2016, Delft, Netherlands. ⟨hal-01406337⟩

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