Procedure for quantitatively comparing the syntactic coverage of English grammars
HLT '91 Proceedings of the workshop on Speech and Natural Language
Natural language parsing as statistical pattern recognition
Natural language parsing as statistical pattern recognition
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Three generative, lexicalised models for statistical parsing
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
Principle-based parsing without overgeneration
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
PRINCIPAR: an efficient, broad-coverage, principle-based parser
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
A dependency-based method for evaluating broad-coverage parsers
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
PEAS, the first instantiation of a comparative framework for evaluating parsers of French
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
FAME: a functional annotation meta-scheme for multi-modal and multi-lingual parsing evaluation
ASSESSEVALNLP '99 Proceedings of a Symposium on Computer Mediated Language Assessment and Evaluation in Natural Language Processing
Sentence diagram generation using dependency parsing
ACLstudent '09 Proceedings of the ACL-IJCNLP 2009 Student Research Workshop
Wide-coverage parsing of speech transcripts
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Cat3LB and Cast3LB: from constituents to dependencies
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Evaluating the performance of the survey parser with the NIST scheme
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Robust conversion of CCG derivations to phrase structure trees
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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With the emergence of broad-coverage parsers, quantitative evaluation of parsers becomes increasingly more important. We propose a dependency-based method for evaluating broad-coverage parsers that offers more meaningful performance measures than previous approaches. We also present a structural pattern-matching mechanism that can be used to eliminate inconsequential differences among different parse trees. Previous evaluation methods have only evaluated the overall performance of parsers. The dependency-based method can also evaluate parsers with respect to different kinds of grammatical relationships or different types of lexical categories. An algorithm for transforming constituency trees into dependency trees is presented, which makes the evaluation method applicable to both constituency grammars and dependency grammars.