{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:56:34Z","timestamp":1750308994016,"version":"3.41.0"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2007,4,1]],"date-time":"2007-04-01T00:00:00Z","timestamp":1175385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Transactions on Asian Language Information Processing"],"published-print":{"date-parts":[[2007,4]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1145\/1227850.1227852","type":"journal-article","created":{"date-parts":[[2007,6,6]],"date-time":"2007-06-06T14:37:11Z","timestamp":1181140631000},"page":"2","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Using data mining techniques and rough set theory for language modeling"],"prefix":"10.1145","volume":"6","author":[{"given":"Yong","family":"Chen","sequence":"first","affiliation":[{"name":"University of Hong Kong, The 54th Research Institute of CTE, China and Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwok-Ping","family":"Chan","sequence":"additional","affiliation":[{"name":"University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2007,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/170035.170072"},{"key":"e_1_2_1_2_1","first-page":"262","article-title":"Extended multi-word trigger pair language model using data mining technique. 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B'S thesis , Massachusetts Institute of Technology , Cambridge, MA . Lau, R. 1993. Maximum likelihood maximum entropy trigger language model. B'S thesis, Massachusetts Institute of Technology, Cambridge, MA."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.192463"},{"volume-title":"Proceedings of 19th International Conference of the North American Fuzzy Information Processing Society","author":"Mohabey A.","key":"e_1_2_1_6_1","unstructured":"Mohabey , A. and Ray , A. K . 2000. Rough set theory based segmentation of color images . In Proceedings of 19th International Conference of the North American Fuzzy Information Processing Society . Atlanta, GA. 338--342. Mohabey, A. and Ray, A. K. 2000. Rough set theory based segmentation of color images. In Proceedings of 19th International Conference of the North American Fuzzy Information Processing Society. 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In Proceedings of the 4th World Congress on Intelligent Control and Automation . Shanghai, China. 426--431. Wei, J. M., Huang, D., Wang, S. Q., and Ma, Z. Y. 2002. Rough set-based decision tree. In Proceedings of the 4th World Congress on Intelligent Control and Automation. 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