Tagging English text with a probabilistic model
Computational Linguistics
Ambiguity packing in constraint-based parsing: practical results
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
More accurate tests for the statistical significance of result differences
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Efficient deep processing of Japanese
COLING '02 Proceedings of the 3rd workshop on Asian language resources and international standardization - Volume 12
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
How much can part-of-speech tagging help parsing?
Natural Language Engineering
Effective self-training for parsing
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Computational Linguistics
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
Feature forest models for probabilistic hpsg parsing
Computational Linguistics
Adapting a lexicalized-grammar parser to contrasting domains
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Efficiency in unification-based N-best parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Minimized models and grammar-informed initialization for supertagging with highly ambiguous lexicons
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Fast, greedy model minimization for unsupervised tagging
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Cross-Domain Effects on Parse Selection for Precision Grammars
Research on Language and Computation
Learning structural dependencies of words in the Zipfian tail
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
Minimally supervised domain-adaptive parse reranking for relation extraction
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
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Parser disambiguation with precision grammars generally takes place via statistical ranking of the parse yield of the grammar using a supervised parse selection model. In the standard process, the parse selection model is trained over a hand-disambiguated treebank, meaning that without a significant investment of effort to produce the treebank, parse selection is not possible. Furthermore, as treebanking is generally streamlined with parse selection models, creating the initial treebank without a model requires more resources than subsequent treebanks. In this work, we show that, by taking advantage of the constrained nature of these HPSG grammars, we can learn a discriminative parse selection model from raw text in a purely unsupervised fashion. This allows us to bootstrap the treebanking process and provide better parsers faster, and with less resources.