LEADER 05596nam 2200709 450 001 9910819095203321 005 20200520144314.0 010 $a1-119-00265-6 010 $a1-119-00252-4 035 $a(CKB)3710000000205233 035 $a(EBL)1752712 035 $a(SSID)ssj0001407560 035 $a(PQKBManifestationID)11811693 035 $a(PQKBTitleCode)TC0001407560 035 $a(PQKBWorkID)11412089 035 $a(PQKB)11264086 035 $a(OCoLC)886940632 035 $a(MiAaPQ)EBC1752712 035 $a(Au-PeEL)EBL1752712 035 $a(CaPaEBR)ebr10899820 035 $a(CaONFJC)MIL632025 035 $a(OCoLC)885019380 035 $a(PPN)191455458 035 $a(EXLCZ)993710000000205233 100 $a20140908h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aComparable corpora and computer-assisted translation /$fEstelle Maryline Delpech ; series editor, Narendra Jussien 210 1$aLondon, England ; :$cHoboken, New Jersey :$ciSTE :$cWiley,$d2014. 210 4$dİ2014 215 $a1 online resource (xiv, 287 pages) 225 0 $aCognitive Science and Knowledge Management Series 300 $aDescription based upon print version of record. 311 $a1-84821-689-0 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Acknowledgments; Introduction; PART 1: Applicative and Scientific Context; Chapter 1: Leveraging Comparable Corpora for Computer-assisted Translation ; 1.1. Introduction; 1.2. From the beginnings of machine translation to comparable corpora processing; 1.2.1. The dawn of machine translation; 1.2.2. The development of computer-assisted translation; 1.2.3. Drawbacks of parallel corpora and advantages of comparable corpora; 1.2.4. Difficulties of technical translation; 1.2.5. Industrial context 327 $a1.3. Term alignment from comparable corpora: a state-of-the-art1.3.1. Distributional approach principle; 1.3.2. Term alignment evaluation; 1.3.2.1. Precision at rank N or TopN; 1.3.2.2. MRR; 1.3.2.3. MAP; 1.3.3. Improvement and variants of the distributional approach; 1.3.3.1. Favoring distributional symmetry; 1.3.3.2. Using syntactic contexts; 1.3.3.3. Relying on trusted elements; 1.3.3.4. Improving semantic information representation; 1.3.3.5. Using second-order semantic affinities; 1.3.3.6. Improving the bilingual resource with semantic classes; 1.3.3.7. Translating polylexical units 327 $a1.3.4. Influence of data and parameters on alignment quality1.3.4.1. Data; 1.3.4.2. Parameters; 1.3.5. Limits of the distributional approach; 1.4. CAT software prototype for comparable corpora processing; 1.4.1. Implementation of a term alignment method; 1.4.1.1. Implementation and data; 1.4.1.2. Extraction of the terms to be aligned; 1.4.1.3. Collecting context vectors; 1.4.1.3.1. Monolexical term context vectors; 1.4.1.4. Polylexical term context vectors; 1.4.1.5. Translation of the source context vectors; 1.4.1.6. Term alignment; 1.4.2. Terminological records extraction 327 $a1.4.3. Lexicon consultation interface1.5. Summary; Chapter 2: User-Centered Evaluation of Lexicons Extracted from Comparable Corpora; 2.1. Introduction; 2.2. Translation quality evaluation methodologies; 2.2.1. Machine translation evaluation; 2.2.1.1. Automatic evaluation measures; 2.2.1.2. Human MT evaluation; 2.2.2. Human translation evaluation; 2.2.2.1. Quantitative models; 2.2.2.2. Non-quantitative models; 2.2.3. Discussion; 2.3. Design and experimentation of a user-centered evaluation; 2.3.1. Methodological aspects; 2.3.1.1. Evaluation criteria and purpose 327 $a2.3.1.2. Subject matter expertise2.3.1.3. Basis for comparison; 2.3.2. Experimentation protocol; 2.3.2.1. Data; 2.3.2.1.1. Comparable corpora and extracted lexica; 2.3.2.1.2. Texts to be translated; 2.3.2.1.3. Resources used in the translation situation; 2.3.2.1.4. Translators and judges; 2.3.2.2. Evaluation progress; 2.3.2.2.1. Translation phase; 2.3.2.2.2. Translation quality evaluation phase; 2.3.3. Results; 2.3.3.1. Lexicons usability; 2.3.3.1.1. Translation speed; 2.3.3.1.2. Use of resources; 2.3.3.1.3. Translators' impressions on the lexicons extracted from comparable corpora 327 $a2.3.3.2. Quality of the generated translations 330 $aComputer-assisted translation (CAT) has always used translation memories, which require the translator to have a corpus of previous translations that the CAT software can use to generate bilingual lexicons. This can be problematic when the translator does not have such a corpus, for instance, when the text belongs to an emerging field. To solve this issue, CAT research has looked into the leveraging of comparable corpora, i.e. a set of texts, in two or more languages, which deal with the same topic but are not translations of one another. This work had two primary objectives. The first is to 410 0$aISTE 606 $aComputational linguistics 606 $aCorpora (Linguistics) 606 $aTranslators (Computer programs) 615 0$aComputational linguistics. 615 0$aCorpora (Linguistics) 615 0$aTranslators (Computer programs) 676 $a410.285 700 $aDelpech$b Estelle Maryline$01632480 702 $aJussien$b Narendra 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910819095203321 996 $aComparable corpora and computer-assisted translation$93971649 997 $aUNINA