LEADER 04177nam 22006375 450 001 9910255000703321 005 20200630221757.0 010 $a3-319-21311-3 024 7 $a10.1007/978-3-319-21311-8 035 $a(CKB)3710000000749187 035 $a(DE-He213)978-3-319-21311-8 035 $a(MiAaPQ)EBC4591910 035 $a(PPN)194515826 035 $a(EXLCZ)993710000000749187 100 $a20160712d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHybrid Approaches to Machine Translation /$fedited by Marta R. Costa-jussą, Reinhard Rapp, Patrik Lambert, Kurt Eberle, Rafael E. Banchs, Bogdan Babych 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (IX, 205 p. 45 illus., 18 illus. in color.) 225 1 $aTheory and Applications of Natural Language Processing,$x2192-032X 311 $a3-319-21310-5 320 $aIncludes bibliographical references at the end of each chapters. 327 $aPreface -- Foreword -- Chapter 1. Hybrid Machine Translation Overview -- Part 1: Adding Linguistics into SMT -- Chapter 2. Controllent Ascent: Imbuing Statistical MT with Linguistic knowledge -- Chapter 3. Hybrid Word Alignment -- Chapter 4. Syntax in SMT -- Part 2. Using Machine Learning in MT -- Chapter 5. Machine Learning in RBMT -- Chapter 6. Language-Independent Hybrid MT -- Part 3. Hybrid NLP tools useful for MT -- Chapter 7. Use of Dependency Parsers in MT -- Chapter 8. Word Sense Disambiguation in MT. . 330 $aThis volume provides an overview of the field of Hybrid Machine Translation (MT) and presents some of the latest research conducted by linguists and practitioners from different multidisciplinary areas. Nowadays, most important developments in MT are achieved by combining data-driven and rule-based techniques. These combinations typically involve hybridization of different traditional paradigms, such as the introduction of linguistic knowledge into statistical approaches to MT, the incorporation of data-driven components into rule-based approaches, or statistical and rule-based pre- and post-processing for both types of MT architectures. The book is of interest primarily to MT specialists, but also ? in the wider fields of Computational Linguistics, Machine Learning and Data Mining ? to translators and managers of translation companies and departments who are interested in recent developments concerning automated translation tools. 410 0$aTheory and Applications of Natural Language Processing,$x2192-032X 606 $aNatural language processing (Computer science) 606 $aComputational linguistics 606 $aTranslation and interpretation 606 $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040 606 $aComputational Linguistics$3https://scigraph.springernature.com/ontologies/product-market-codes/N22000 606 $aTranslation$3https://scigraph.springernature.com/ontologies/product-market-codes/N47000 615 0$aNatural language processing (Computer science). 615 0$aComputational linguistics. 615 0$aTranslation and interpretation. 615 14$aNatural Language Processing (NLP). 615 24$aComputational Linguistics. 615 24$aTranslation. 676 $a006.35 702 $aCosta-jussą$b Marta R$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRapp$b Reinhard$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLambert$b Patrik$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aEberle$b Kurt$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBanchs$b Rafael E$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBabych$b Bogdan$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910255000703321 996 $aHybrid Approaches to Machine Translation$92007730 997 $aUNINA