|Titre||Improving a symbolic parser through partially supervised learning|
|Publication Type||Conference Paper|
|Année de publication||2013|
|Auteurs||De La Clergerie, ÉVillemonte|
|Nom de la conférence||The 13th International Conference on Parsing Technologies (IWPT)|
|Lieu de la conférence||Nara, Japon|
|Mots-clés||parsing, partially supervised machine learning, statitistical parsing, symbolic parsing, Tree Adjoining Grammar|
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.