The ATIS spoken language systems pilot corpus
HLT '90 Proceedings of the workshop on Speech and Natural Language
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Parsing the Wall Street Journal with the inside-outside algorithm
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
An empirical evaluation of Probabilistic Lexicalized Tree Insertion Grammars
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Inside-outside reestimation from partially bracketed corpora
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Supervised and unsupervised PCFG adaptation to novel domains
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Using existing systems to supplement small amounts of annotated grammatical relations training data
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Sample selection for statistical grammar induction
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Automated extraction of Tree-Adjoining Grammars from treebanks
Natural Language Engineering
Reranking and self-training for parser adaptation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Skeletons in the parser: using a shallow parser to improve deep parsing
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Partial training for a lexicalized-grammar parser
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Trada: tree based ranking function adaptation
Proceedings of the 17th ACM conference on Information and knowledge management
Innovations in Natural Language Document Processing for Requirements Engineering
Innovations for Requirement Analysis. From Stakeholders' Needs to Formal Designs
Semi-supervised training of a statistical parser from unlabeled partially-bracketed data
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
MAP adaptation of stochastic grammars
Computer Speech and Language
Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation
International Journal of Robotics Research
Cross-market model adaptation with pairwise preference data for web search ranking
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Ranking function adaptation with boosting trees
ACM Transactions on Information Systems (TOIS)
Training dependency parser using light feedback
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Corpus-based grammar induction generally relies on hand-parsed training data to learn the structure of the language. Unfortunately, the cost of building large annotated corpora is prohibitively expensive. This work aims to improve the induction strategy when there are few labels in the training data. We show that the most informative linguistic constituents are the higher nodes in the parse trees, typically denoting complex noun phrases and sentential clauses. They account for only 20% of all constituents. For inducing grammars from sparsely labeled training data (e.g., only higher-level constituent labels), we propose an adaptation strategy, which produces grammars that parse almost as well as grammars induced from fully labeled corpora. Our results suggest that for a partial parser to replace human annotators, it must be able to automatically extract higher-level constituents rather than base noun phrases.