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Improving a symbolic parser through partially supervised learning

Abstract : Recently, several statistical parsers have been trained and evaluated on the dependency version of the French TreeBank (FTB). However, older symbolic parsers still exist, including FRMG, a wide coverage TAG parser. It is interesting to compare these different parsers, based on very different approaches, and explore the possibilities of hybridization. In particular, we explore the use of partially supervised learning techniques to improve the performances of FRMG to the levels reached by the statistical parsers.
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Contributor : Eric Villemonte de la Clergerie <>
Submitted on : Saturday, November 2, 2013 - 10:58:38 PM
Last modification on : Saturday, March 28, 2020 - 2:15:50 AM
Long-term archiving on: : Monday, February 3, 2014 - 4:26:54 AM


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  • HAL Id : hal-00879358, version 1



Éric Villemonte de la Clergerie. Improving a symbolic parser through partially supervised learning. The 13th International Conference on Parsing Technologies (IWPT), Nov 2013, Naria, Japan. ⟨hal-00879358⟩



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