Machine Learning
PCFG models of linguistic tree representations
Computational Linguistics
A statistical parser for Czech
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Recovering latent information in treebanks
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Probabilistic parsing for German using sister-head dependencies
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Is it harder to parse Chinese, or the Chinese Treebank?
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Lexicalization in crosslinguistic probabilistic parsing: the case of French
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
What to do when lexicalization fails: parsing German with suffix analysis and smoothing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Using machine-learning to assign function labels to parser output for Spanish
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Discriminative reranking for semantic parsing
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Parsing the SynTagRus treebank of Russian
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
(Meta-) evaluation of machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Statistical parsing of morphologically rich languages (SPMRL): what, how and whither
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Improving Arabic dependency parsing with lexical and inflectional morphological features
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Application of different techniques to dependency parsing of Basque
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Better Arabic parsing: baselines, evaluations, and analysis
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Improving Arabic dependency parsing with form-based and functional morphological features
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models
The Journal of Machine Learning Research
Morphological features for parsing morphologically-rich languages: a case of Arabic
SPMRL '11 Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages
Preprocessing of informal mathematical discourse in context ofcontrolled natural language
Proceedings of the 21st ACM international conference on Information and knowledge management
Dependency parsing of modern standard arabic with lexical and inflectional features
Computational Linguistics
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We present two methods for incorporating detailed features in a Spanish parser, building on a baseline model that is a lexicalized PCFG. The first method exploits Spanish morphology, and achieves an F1 constituency score of 83.6%. This is an improvement over 81.2% accuracy for the baseline, which makes little or no use of morphological information. The second model uses a reranking approach to add arbitrary global features of parse trees to the morphological model. The reranking model reaches 85.1% F1 accuracy on the Spanish parsing task. The resulting model for Spanish parsing combines an approach that specifically targets morphological information with an approach that makes use of general structural features.