Benchmarking of Statistical Dependency Parsers for French

Marie Candito (1) , Joakim Nivre (2) , Pascal Denis (1) , Enrique Henestroza Anguiano (1)

1 ALPAGE - Analyse Linguistique Profonde à Grande Echelle ; Large-scale deep linguistic processing
2 Uppsala University

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.

Mots clés: statistical parsing; dependency parsing; semi-supervised learning

Domaines: Informatique et langage [cs.CL]; Traitement du texte et du document

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⟩