Van Huyssteen, GBDavel, MH2010-07-232010-07-232010-06Van Huyssteen, GB, and Davel MH. 2010. Learning rules and categorization networks for language standardization. Human Language Technologies, Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2-4 June 2010, Los Angeles, California, USA, pp 39-461932432655http://hdl.handle.net/10204/4129Human Language Technologies, Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2-4 June 2010, Los Angeles, California, USAIn this research, the authors use machine learning techniques to provide solutions for descriptive linguists in the domain of language standardization. With regard to the personal name construction in Afrikaans, the authors perform function learning from word pairs using the Default and Refine algorithm. The authors demonstrate how the extracted rules can be used to identify irregularities in previously standardized constructions and to predict new forms of unseen words. In addition, the authors defined a generic, automated process that allows them to extract constructional schemas and present these visually as categorization networks, similar to what is often being used in Cognitive Grammar. The authors conclude that computational modeling of constructions can contribute to new descriptive linguistic insights, and to practical language solutions.enHuman language technologiesHLTDefault refine algorithmAfrikaansComputational linguisticsCognitive grammarLanguage standardizationCategorization networksConference PresentationVan Huyssteen, G., & Davel, M. (2010). Learning rules and categorization networks for language standardization. Association for Computational Linguistics. http://hdl.handle.net/10204/4129Van Huyssteen, GB, and MH Davel. "Learning rules and categorization networks for language standardization." (2010): http://hdl.handle.net/10204/4129Van Huyssteen G, Davel M, Learning rules and categorization networks for language standardization; Association for Computational Linguistics; 2010. http://hdl.handle.net/10204/4129 .TY - Conference Presentation AU - Van Huyssteen, GB AU - Davel, MH AB - In this research, the authors use machine learning techniques to provide solutions for descriptive linguists in the domain of language standardization. With regard to the personal name construction in Afrikaans, the authors perform function learning from word pairs using the Default and Refine algorithm. The authors demonstrate how the extracted rules can be used to identify irregularities in previously standardized constructions and to predict new forms of unseen words. In addition, the authors defined a generic, automated process that allows them to extract constructional schemas and present these visually as categorization networks, similar to what is often being used in Cognitive Grammar. The authors conclude that computational modeling of constructions can contribute to new descriptive linguistic insights, and to practical language solutions. DA - 2010-06 DB - ResearchSpace DP - CSIR KW - Human language technologies KW - HLT KW - Default refine algorithm KW - Afrikaans KW - Computational linguistics KW - Cognitive grammar KW - Language standardization KW - Categorization networks LK - https://researchspace.csir.co.za PY - 2010 SM - 1932432655 T1 - Learning rules and categorization networks for language standardization TI - Learning rules and categorization networks for language standardization UR - http://hdl.handle.net/10204/4129 ER -