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Article: Using data mining techniques and rough set theory for language modeling

TitleUsing data mining techniques and rough set theory for language modeling
Authors
Chen, YChan, KP
KeywordsChinese character recognizer
Postprocessing
Issue Date2007
PublisherAssociation for Computing Machinery, Inc.
Citation
Acm Transactions On Asian Language Information Processing, 2007, v. 6 n. 1 How to Cite?
DOI: http://dx.doi.org/10.1145/1227850.1227852
AbstractIn this article, we propose a new postprocessing strategy, word suggestion, based on a multiple word trigger-pair language model for Chinese character recognizers. With the word suggestion strategy, Chinese character recognizers may even achieve a recognition rate greater than the top-n candidate recognition rate. To construct the multiple word trigger-pair model, data mining techniques are used to alleviate the intensive computation problem. Furthermore, rough set theory is first used in the study to discover negatively correlated relationships between words in order to prevent introducing wrong words in the process of word suggestion. © 2007 ACM.
Persistent Identifierhttp://hdl.handle.net/10722/89180
ISSN
1530-0226
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorChen, Yen_HK
dc.contributor.authorChan, KPen_HK
dc.date.accessioned2010-09-06T09:53:23Z-
dc.date.available2010-09-06T09:53:23Z-
dc.date.issued2007en_HK
dc.identifier.citationAcm Transactions On Asian Language Information Processing, 2007, v. 6 n. 1en_HK
dc.identifier.issn1530-0226en_HK
dc.identifier.urihttp://hdl.handle.net/10722/89180-
dc.description.abstractIn this article, we propose a new postprocessing strategy, word suggestion, based on a multiple word trigger-pair language model for Chinese character recognizers. With the word suggestion strategy, Chinese character recognizers may even achieve a recognition rate greater than the top-n candidate recognition rate. To construct the multiple word trigger-pair model, data mining techniques are used to alleviate the intensive computation problem. Furthermore, rough set theory is first used in the study to discover negatively correlated relationships between words in order to prevent introducing wrong words in the process of word suggestion. © 2007 ACM.en_HK
dc.languageengen_HK
dc.publisherAssociation for Computing Machinery, Inc.en_HK
dc.relation.ispartofACM Transactions on Asian Language Information Processingen_HK
dc.rightsACM Transactions on Asian Language Information Processing. Copyright © Association for Computing Machinery, Inc.en_HK
dc.subjectChinese character recognizeren_HK
dc.subjectPostprocessingen_HK
dc.titleUsing data mining techniques and rough set theory for language modelingen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0730-0301&volume=6&spage=&epage=&date=2007&atitle=Using+Data+Mining+Techniques+And+Rough+Set+Theory+For+Language+Modelingen_HK
dc.identifier.emailChan, KP:kpchan@cs.hku.hken_HK
dc.identifier.authorityChan, KP=rp00092en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1145/1227850.1227852en_HK
dc.identifier.scopuseid_2-s2.0-34247263912en_HK
dc.identifier.hkuros129384en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34247263912&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume6en_HK
dc.identifier.issue1en_HK
dc.identifier.eissn1558-3430-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChen, Y=7601437873en_HK
dc.identifier.scopusauthoridChan, KP=7406032820en_HK
dc.identifier.issnl1530-0226-

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