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Benchmarking of Statistical Dependency Parsers for French

Abstract : We compare the performance of three statistical parsing architectures on the problem of deriving typed dependency structures for French. The architectures are based on PCFGs with latent variables, graph-based dependency parsing and transition-based dependency parsing, respectively. We also study the influence of three types of lexical information: lemmas, morphological features, and word clusters. The results show that all three systems achieve competitive performance, with a best labeled attachment score over 88%. All three parsers benefit from the use of automatically derived lemmas, while morphological features seem to be less important. Word clusters have a positive effect primarily on the latent variable parser.
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Contributor : Marie Candito <>
Submitted on : Tuesday, September 7, 2010 - 3:36:54 PM
Last modification on : Friday, March 27, 2020 - 4:02:25 AM
Document(s) archivé(s) le : Wednesday, December 8, 2010 - 2:29:53 AM


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  • HAL Id : hal-00514815, version 1



Marie Candito, Joakim Nivre, Pascal Denis, Enrique Henestroza Anguiano. Benchmarking of Statistical Dependency Parsers for French. 23rd International Conference on Computational Linguistics - COLING 2010, Aug 2010, Beijing, China. pp.108-116. ⟨hal-00514815⟩



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