Accurate Deep Syntactic Parsing of Graphs: The Case of French

Corentin Ribeyre, Eric Villemonte de la Clergerie, Djamé Seddah


Abstract
Parsing predicate-argument structures in a deep syntax framework requires graphs to be predicted. Argument structures represent a higher level of abstraction than the syntactic ones and are thus more difficult to predict even for highly accurate parsing models on surfacic syntax. In this paper we investigate deep syntax parsing, using a French data set (Ribeyre et al., 2014a). We demonstrate that the use of topologically different types of syntactic features, such as dependencies, tree fragments, spines or syntactic paths, brings a much needed context to the parser. Our higher-order parsing model, gaining thus up to 4 points, establishes the state of the art for parsing French deep syntactic structures.
Anthology ID:
L16-1566
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3563–3568
Language:
URL:
https://www.aclweb.org/anthology/L16-1566
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/L16-1566.pdf