Because syntax does matter: Improving predicate-argument structures parsing with syntactic features

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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 graphbased parser (Martins and Almeida, 2014), we establish a new state-of-the-art on these data sets.

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Ribeyre, C., De La Clergerie, E. V., & Seddah, D. (2015). Because syntax does matter: Improving predicate-argument structures parsing with syntactic features. In NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 64–74). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/n15-1007

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