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Hybrid Approaches to Machine Translation
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Nowadays most important developments in machine translation (MT) are achieved via combining data-driven and rule-based techniques. These combinations typically involve hybridization of different traditional paradigms, such as the introduction of linguistic knowledge into statistical MT paradigms, or the incorporation of data-driven components into rule-based paradigms, or statistical and rule-based pre- and post-processing for both types of MT architectures. The volume provides an overview of the field, as well as the latest relevant research conducted by linguists and practitioners from different multidisciplinary areas who work in these directions of hybrid MT. The book will be of interest primarily for the specialists in Machine Translation, but also - in a wider field of Computational Linguistic, Machine Learning and Data Mining - for translators and managers of translation companies and departments who are interested in most recent developments in tools that automate translation processes.
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