Procedure for quantitatively comparing the syntactic coverage of English grammars
HLT '91 Proceedings of the workshop on Speech and Natural Language
The syntactic process
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A DOP model for semantic interpretation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
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
Robust, applied morphological generation
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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
Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
SemEval-2007 task 10: English lexical substitution task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Investigations on word senses and word usages
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Unbounded dependency recovery for parser evaluation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
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Parser Evaluation using Textual Entailments (PETE) is a shared task in the SemEval-2010 Evaluation Exercises on Semantic Evaluation. The task involves recognizing textual entailments based on syntactic information alone. PETE introduces a new parser evaluation scheme that is formalism independent, less prone to annotation error, and focused on semantically relevant distinctions. This paper describes the PETE task, gives an error analysis of the top-performing Cambridge system, and introduces a standard entailment module that can be used with any parser that outputs Stanford typed dependencies.