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  • The goal of this dissertation is to explore models, methods and methodologies for machine learning of the compact and effective storage of empirical data in the areas of language engineering and computer typesetting, with a focus on the massive exception handling. Research has focused on the pattern-driven approach. The whole methodology of so called \stress{competing patterns} capable of handling exceptions to be found so widely in natural language data and computer typesetting, is further developed. Competing patterns can store \stress{context dependent} information and can be learnt from data, or written by experts, or combined together. In the first part of the thesis, the theory of competing patterns is built; competing patterns are defined, cornerstones of methodology based on stratified sampling, bootstrapping and problem modeling by competing patterns are described.
  • The goal of this dissertation is to explore models, methods and methodologies for machine learning of the compact and effective storage of empirical data in the areas of language engineering and computer typesetting, with a focus on the massive exception handling. Research has focused on the pattern-driven approach. The whole methodology of so called \stress{competing patterns} capable of handling exceptions to be found so widely in natural language data and computer typesetting, is further developed. Competing patterns can store \stress{context dependent} information and can be learnt from data, or written by experts, or combined together. In the first part of the thesis, the theory of competing patterns is built; competing patterns are defined, cornerstones of methodology based on stratified sampling, bootstrapping and problem modeling by competing patterns are described. (en)
Title
  • Competing Patterns in Language Engineering and Computer Typesetting
  • Competing Patterns in Language Engineering and Computer Typesetting (en)
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  • Competing Patterns in Language Engineering and Computer Typesetting
  • Competing Patterns in Language Engineering and Computer Typesetting (en)
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  • RIV/00216224:14330/05:00024493!RIV10-MSM-14330___
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  • P(1ET200190513), S
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  • RIV/00216224:14330/05:00024493
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  • competing patterns; context-sensitive patterns; machine learning; natural language engineering; hyphenation; [Thai text] segmentation; disambiguation; formal concept analysis; part of speech tagging (en)
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  • Sojka, Petr
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  • 14330
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