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Conference Papers Year : 2016

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

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.

Domains

Machine Learning [cs.LG] Artificial Intelligence [cs.AI] Bioinformatics [q-bio.QM]
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François Coste  : Connect in order to contact the contributor

https://inria.hal.science/hal-01406337

Submitted on : Thursday, December 1, 2016-9:47:54 AM

Last modification on : Friday, March 24, 2023-2:53:03 PM

Long-term archiving on: Monday, March 20, 2017-4:48:23 PM

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Dates and versions

hal-01406337 , version 1 (01-12-2016)

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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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