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HLT '91 Proceedings of the workshop on Speech and Natural Language
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
New figures of merit for best-first probabilistic chart parsing
Computational Linguistics
PCFG models of linguistic tree representations
Computational Linguistics
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NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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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
Automatic compensation for parser figure-of-merit flaws
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Statistical parsing with a context-free grammar and word statistics
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Time as a measure of parsing efficiency
Proceedings of the COLING-2000 Workshop on Efficiency In Large-Scale Parsing Systems
Probabilistic top-down parsing and language modeling
Computational Linguistics
Natural Language Engineering
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COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Reducing grammar errors for translated english sentences
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Time as a measure of parsing efficiency
Proceedings of the COLING-2000 Workshop on Efficiency In Large-Scale Parsing Systems
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Very little attention has been paid to the comparison of efficiency between high accuracy statistical parsers. This paper proposes one machine-independent metric that is general enough to allow comparisons across very different parsing architectures. This metric, which we call "events considered", measures the number of "events", however they are defined for a particular parser, for which a probability must be calculated, in order to find the parse. It is applicable to single-pass or multi-stage parsers. We discuss the advantages of the metric, and demonstrate its usefulness by using it to compare two parsers which differ in several fundamental ways.