2010 BenchmarkingofStatisticalDepend

• (Candito et al., 2010) ⇒ Marie Candito, Joakim Nivre, Pascal Denis, and Enrique Henestroza Anguiano. (2010). “Benchmarking of Statistical Dependency Parsers for French.” In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters.

Subject Headings: French Syntatic Parser.

Cited By

• http://scholar.google.com/scholar?q=%222010%22+Benchmarking+of+Statistical+Dependency+Parsers+for+French
• http://dl.acm.org/citation.cfm?id=1944566.1944579&preflayout=flat#citedby

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 BenchmarkingofStatisticalDepend	Joakim Nivre Marie Candito Pascal Denis Enrique Henestroza Anguiano			Benchmarking of Statistical Dependency Parsers for French						2010