Procedure for quantitatively comparing the syntactic coverage of English grammars
HLT '91 Proceedings of the workshop on Speech and Natural Language
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Probabilistic top-down parsing and language modeling
Computational Linguistics
Exploiting syntactic structure for language modeling
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Accurate unlexicalized parsing
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
Parsing and question classification for question answering
ODQA '01 Proceedings of the workshop on Open-domain question answering - Volume 12
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Clause restructuring for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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
Scalable inference and training of context-rich syntactic translation models
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Natural language generation using an information-slim representation
Natural language generation using an information-slim representation
Design of a multi-lingual, parallel-processing statistical parsing engine
HLT '02 Proceedings of the second international conference on Human Language Technology Research
SPMT: statistical machine translation with syntactified target language phrases
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
MAP adaptation of stochastic grammars
Computer Speech and Language
A cloze test authoring system and its automation
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
Automatic selection of high quality parses created by a fully unsupervised parser
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Automatic domain adaptation for parsing
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
TrustRank: inducing trust in automatic translations via ranking
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Profiting from mark-up: hyper-text annotations for guided parsing
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Improved fully unsupervised parsing with zoomed learning
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Effective measures of domain similarity for parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
ULISSE: an unsupervised algorithm for detecting reliable dependency parses
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Comparing the use of edited and unedited text in parser self-training
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
ReliAble dependency arc recognition
Expert Systems with Applications: An International Journal
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Statistical parsers have become increasingly accurate, to the point where they are useful in many natural language applications. However, estimating parsing accuracy on a wide variety of domains and genres is still a challenge in the absence of gold-standard parse trees. In this paper, we propose a technique that automatically takes into account certain characteristics of the domains of interest, and accurately predicts parser performance on data from these new domains. As a result, we have a cheap (no annotation involved) and effective recipe for measuring the performance of a statistical parser on any given domain.