Class-based n-gram models of natural language
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Pseudo-projectivity: a polynomially parsable non-projective dependency grammar
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Inductive Dependency Parsing (Text, Speech and Language Technology)
Inductive Dependency Parsing (Text, Speech and Language Technology)
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Discriminative learning and spanning tree algorithms for dependency parsing
Discriminative learning and spanning tree algorithms for dependency parsing
Design of a multi-lingual, parallel-processing statistical parsing engine
HLT '02 Proceedings of the second international conference on Human Language Technology Research
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Algorithms for deterministic incremental dependency parsing
Computational Linguistics
Integrated morphological and syntactic disambiguation for Modern Hebrew
COLING ACL '06 Proceedings of the 21st International Conference on computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Multilingual dependency analysis with a two-stage discriminative parser
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
The PaGe 2008 shared task on parsing German
PaGe '08 Proceedings of the Workshop on Parsing German
A dependency-based method for evaluating broad-coverage parsers
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Improving generative statistical parsing with semi-supervised word clustering
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Cross parser evaluation and tagset variation: a French treebank study
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Parse correction with specialized models for difficult attachment types
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Leveraging different meronym discovery methods for bridging resolution in french
DAARC'11 Proceedings of the 8th international conference on Anaphora Processing and Applications
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
A word clustering approach to domain adaptation: effective parsing of biomedical texts
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
A comparison of Chinese parsers for stanford dependencies
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
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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.