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Article: Using data mining techniques and rough set theory for language modeling
Title | Using data mining techniques and rough set theory for language modeling |
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Authors | |
Keywords | Chinese character recognizer Postprocessing |
Issue Date | 2007 |
Publisher | Association for Computing Machinery, Inc. |
Citation | Acm Transactions On Asian Language Information Processing, 2007, v. 6 n. 1 How to Cite? |
Abstract | In 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 Identifier | http://hdl.handle.net/10722/89180 |
ISSN | |
References |
DC Field | Value | Language |
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dc.contributor.author | Chen, Y | en_HK |
dc.contributor.author | Chan, KP | en_HK |
dc.date.accessioned | 2010-09-06T09:53:23Z | - |
dc.date.available | 2010-09-06T09:53:23Z | - |
dc.date.issued | 2007 | en_HK |
dc.identifier.citation | Acm Transactions On Asian Language Information Processing, 2007, v. 6 n. 1 | en_HK |
dc.identifier.issn | 1530-0226 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/89180 | - |
dc.description.abstract | In 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.language | eng | en_HK |
dc.publisher | Association for Computing Machinery, Inc. | en_HK |
dc.relation.ispartof | ACM Transactions on Asian Language Information Processing | en_HK |
dc.rights | ACM Transactions on Asian Language Information Processing. Copyright © Association for Computing Machinery, Inc. | en_HK |
dc.subject | Chinese character recognizer | en_HK |
dc.subject | Postprocessing | en_HK |
dc.title | Using data mining techniques and rough set theory for language modeling | en_HK |
dc.type | Article | en_HK |
dc.identifier.openurl | http://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+Modeling | en_HK |
dc.identifier.email | Chan, KP:kpchan@cs.hku.hk | en_HK |
dc.identifier.authority | Chan, KP=rp00092 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1145/1227850.1227852 | en_HK |
dc.identifier.scopus | eid_2-s2.0-34247263912 | en_HK |
dc.identifier.hkuros | 129384 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-34247263912&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 6 | en_HK |
dc.identifier.issue | 1 | en_HK |
dc.identifier.eissn | 1558-3430 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Chen, Y=7601437873 | en_HK |
dc.identifier.scopusauthorid | Chan, KP=7406032820 | en_HK |
dc.identifier.issnl | 1530-0226 | - |