Atwell, ES (1988) Grammatical analysis of English by statistical pattern-recognition. In: Pattern Recognition: Lecture Notes in Computer Science. The 4th International Conference on Pattern Recognition, 28-30 Mar 1988, Cambridge, UK. Springer , 626 - 635. ISBN 978-3-540-19036-3
Abstract
Artificial Intelligence and Computational Linguistics researchers are currently debating the value of 'Deep' knowledge-representations in language processing and related computations. Incorporating deep knowledge as well as surface statistical pattern recognition requires much greater processing, but it has been assumed that, for many applications of Artificial Intelligence, purely surface statistical analyses cannot yield useful results. One NLP application provides a counter-argument to this widespread tenet: a system for grammatical error detection, using only probabilistic , Markovian pattern-matching was devised, and in tests compared favourably with a much larger system which computed deep grammatical analyses of each sentence. Those who argue that statistical pattern recognition has no place in Computational Linguistics or Artificial Intelligence have still to prove their case.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Algorithms; classification; cognition; computer vision; image analysis; image segmentation; knowledge; knowledge base; learning; optical character recognition (OCR); pattern recognition; perception; robot; robotics; speech recognition |
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: | 11 Dec 2014 11:41 |
Last Modified: | 21 Feb 2024 13:33 |
Published Version: | http://link.springer.com/chapter/10.1007/3-540-190... |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/3-540-19036-8_63 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:81877 |