Atwell, E (1987) How to detect grammatical errors in a text without parsing it. In: Maegaard, B, (ed.) EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics. Third Conference of the European Chapter of the Association for Computational Linguistics, 01-03 Apr 1987, University of Copenhagen, Denamrk. The Association for Computer Linguistics , 38 - 45.
Abstract
The Constituent Likelihood Automatic Word-tagging System (CLAWS) was originally designed for the low-level grammatical analysis of the million-word LOB Corpus of English text samples. CLAWS does not attempt a full parse, but uses a firat-order Markov model of language to assign word-class labels to words. CLAWS can be modified to detect grammatical errors, essentially by flagging unlikely word-class transitions in the input text. This may seem to be an intuitively implausible and theoretically inadequate model of natural language syntax, but nevertheless it can successfully pinpoint most grammatical errors in a text. Several modifications to CLAWS have been explored. The resulting system cannot detect all errors in typed documents; but then neither do far more complex systems, which attempt a full parse, requiting much greater computation.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | Atwell, E (c) 1987, University of Leeds. Reproduced with permission from the copyright holders. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Dec 2014 12:56 |
Last Modified: | 21 Feb 2024 13:32 |
Published Version: | http://aclweb.org/anthology-new/E/E87/ |
Status: | Published |
Publisher: | The Association for Computer Linguistics |
Identification Number: | 10.3115/976858.976865 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:81825 |