Automatic metadata extraction from museum specimen labels
DCMI '08 Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications
Adapting a WSJ-trained parser to grammatically noisy text
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
GenERRate: generating errors for use in grammatical error detection
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
Domain adaptation with artificial data for semantic parsing of speech
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Realistic grammar error simulation using Markov Logic
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Reducing overdetections in a French symbolic grammar checker by classification
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Grammatical error simulation for computer-assisted language learning
Knowledge-Based Systems
Experiments with artificially generated noise for cleansing noisy text
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
Adapting a WSJ trained part-of-speech tagger to noisy text: preliminary results
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
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This article describes how a treebank of ungrammatical sentences can be created from a treebank of well-formed sentences. The treebank creation procedure involves the automatic introduction of frequently occurring grammatical errors into the sentences in an existing treebank, and the minimal transformation of the original analyses in the treebank so that they describe the newly created ill-formed sentences. Such a treebank can be used to test how well a parser is able to ignore grammatical errors in texts (as people do), and can be used to induce a grammar capable of analysing such sentences. This article demonstrates these two applications using the Penn Treebank. In a robustness evaluation experiment, two state-of-the-art statistical parsers are evaluated on an ungrammatical version of Sect. 23 of the Wall Street Journal (WSJ) portion of the Penn treebank. This experiment shows that the performance of both parsers degrades with grammatical noise. A breakdown by error type is provided for both parsers. A second experiment retrains both parsers using an ungrammatical version of WSJ Sections 2–21. This experiment indicates that an ungrammatical treebank is a useful resource in improving parser robustness to grammatical errors, but that the correct combination of grammatical and ungrammatical training data has yet to be determined.