Conventional and associative memory approaches to automatic spelling correction

dc.contributor.author	Cherkassky, Vladimir	en
dc.contributor.author	Βασιλάς, Νικόλαος	el
dc.contributor.author	Brodt, Gregory L.	en
dc.contributor.author	Wechsler, Harry	en
dc.date.accessioned	2015-05-11T18:34:09Z
dc.date.available	2015-05-11T18:34:09Z
dc.date.issued	2015-05-11
dc.identifier.uri	http://hdl.handle.net/11400/10156
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.sciencedirect.com/science/article/pii/0952197692900066	en
dc.subject	Συνειρμική μνήμη
dc.subject	νευρωνικά δίκτυα
dc.subject	ισχυρή ανάκτηση δεδομένων
dc.subject	ορθογράφος
dc.subject	Ταύτιση
dc.subject	Associative storage
dc.subject	Neural networks
dc.subject	robust data retrieval
dc.subject	spelling checkers
dc.subject	string matching
dc.title	Conventional and associative memory approaches to automatic spelling correction	en
heal.type	journalArticle
heal.generalDescription	σε έντυπη μορφή στο γραφείο μου	el
heal.classification	Πληροφορική
heal.classification	Μηχανική υπολογιστών
heal.classification	Computer science
heal.classification	Computer engineering
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	N/A-Μηχανική υπολογιστών
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85029495
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85008824
heal.keywordURI	http://zbw.eu/stw/descriptor/19808-6
heal.identifier.secondary	DOI: 10.1016/0952-1976(92)90006-6
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	1992-05
heal.bibliographicCitation	Cherkassky, V., Vassilas, N., Brodt, G.L. and Wechsler, H. (1992) Conventional and associative memory approaches to automatic spelling correction. Engineering Applications of Artificial Intelligence. [Online] 5 (3), pp.223-237. Available from: http://www.sciencedirect.com [Accessed 11/05/2015]	en
heal.abstract	This paper reviews conventional and emerging associative-memory approaches to robust data retrieval when the input keyword and/or database itself may contain noise (errors). Spelling checking is used as a primary example to illustrate various approaches and to contrast the difference between conventional (algorithmic) techniques and research methods based on neural associative memories. Based on several recent ad hoc models for associative spelling checkers a generic model is proposed that incorporates powerful N-gram encoding for word representation and supervised-learning associative memories. Recent research on associative spelling checkers is summarized and some original results are presented. It is concluded that many neural network models do not provide a practically viable solution for robust data retrieval, due to saturation and scaling problems. However, a combination of conventional and neural approaches is shown to have excellent error correction rates and low computational costs; hence, it can be a good choice for robust data retrieval in large databases.	en
heal.publisher	Elsevier	en
heal.journalName	Engineering Applications of Artificial Intelligence	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true