Because Syntax does Matter: Improving Predicate-Argument Structures Parsing Using Syntactic Features

Abstract : Parsing full-fledged predicate-argument structures in a deep syntax framework requires graphs to be predicted. Using the DeepBank (Flickinger et al., 2012) and the Predicate-Argument Structure treebank (Miyao and Tsujii, 2005) as a test field, we show how transition-based parsers, extended to handle connected graphs, benefit from the use of topologically different syntactic features such as dependencies, tree fragments, spines or syntactic paths, bringing a much needed context to the parsing models, improving notably over long distance dependencies and elided coordinate structures. By confirming this positive impact on an accurate 2nd-order graph-based parser (Martins and Almeida, 2014), we establish a new state-of-the-art on these data sets.
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Communication dans un congrès
Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun 2015, Denver, USA, United States. 2015, Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Corentin Ribeyre, Éric Villemonte de La Clergerie, Djamé Seddah. Because Syntax does Matter: Improving Predicate-Argument Structures Parsing Using Syntactic Features. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun 2015, Denver, USA, United States. 2015, Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 〈hal-01174533〉

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