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
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Intricacies of Collins' Parsing Model
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
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
Spoken language processing: Piecing together the puzzle
Speech Communication
When is self-training effective for parsing?
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Structural correspondence learning for parse disambiguation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Porting a lexicalized-grammar parser to the biomedical domain
Journal of Biomedical Informatics
MAP adaptation of stochastic grammars
Computer Speech and Language
Adapting a probabilistic disambiguation model of an HPSG parser to a new domain
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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Natural language parsing, as one of the central tasks in natural language processing, is widely used in many AI fields. In this paper, we address an issue of parser performance evaluation, particularly its variation across datasets. We propose three simple statistical measures to characterize the datasets and also evaluate their correlation to the parser performance. The results clearly show that different parsers have different performance variation and sensitivity against these measures. The method can be used to guide the choice of natural language parsers for new domain applications, as well as systematic combination for better parsing accuracy.