LEADER 06046nam 22006975 450 001 996465652103316 005 20200703224830.0 010 $a3-540-45820-4 024 7 $a10.1007/3-540-45820-4 035 $a(CKB)1000000000211809 035 $a(SSID)ssj0000324591 035 $a(PQKBManifestationID)11241184 035 $a(PQKBTitleCode)TC0000324591 035 $a(PQKBWorkID)10313384 035 $a(PQKB)10484706 035 $a(DE-He213)978-3-540-45820-3 035 $a(MiAaPQ)EBC3072407 035 $a(PPN)155219405 035 $a(EXLCZ)991000000000211809 100 $a20121227d2002 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Translation: From Research to Real Users$b[electronic resource] $e5th Conference of the Association for Machine Translation in the Americas, AMTA 2002 Tiburon, CA, USA, October 6-12, 2002. Proceedings /$fedited by Stephen D. Richardson 205 $a1st ed. 2002. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2002. 215 $a1 online resource (XXII, 258 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v2499 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-44282-0 320 $aIncludes bibliographical references and index. 327 $aTechnical Papers -- Automatic Rule Learning for Resource-Limited MT -- Toward a Hybrid Integrated Translation Environment -- Adaptive Bilingual Sentence Alignment -- DUSTer: A Method for Unraveling Cross-Language Divergences for Statistical Word-Level Alignment -- Text Prediction with Fuzzy Alignments -- Efficient Integration of Maximum Entropy Lexicon Models within the Training of Statistical Alignment Models -- Using Word Formation Rules to Extend MT Lexicons -- Example-Based Machine Translation via the Web -- Handling Translation Divergences: Combining Statistical and Symbolic Techniques in Generation-Heavy Machine Translation -- Korean-Chinese Machine Translation Based on Verb Patterns -- Merging Example-Based and Statistical Machine Translation: An Experiment -- Classification Approach to Word Selection in Machine Translation -- Better Contextual Translation Using Machine Learning -- Fast and Accurate Sentence Alignment of Bilingual Corpora -- Deriving Semantic Knowledge from Descriptive Texts Using an MT System -- Using a Large Monolingual Corpus to Improve Translation Accuracy -- Semi-automatic Compilation of Bilingual Lexicon Entries from Cross-Lingually Relevant News Articles on WWW News Sites -- Bootstrapping the Lexicon Building Process for Machine Translation between ?New? Languages -- User Studies -- A Report on the Experiences of Implementing an MT System for Use in a Commercial Environment -- Getting the Message In: A Global Company?s Experience with the New Generation of Low-Cost, High Performance Machine Translation Systems -- An Assessment of Machine Translation for Vehicle Assembly Process Planning at Ford Motor Company -- System Descriptions -- Fluent Machines? EliMT System -- LogoMedia TRANSLATE?, Version 2.0 -- Natural Intelligence in a Machine Translation System -- Translation by the Numbers: Language Weaver -- A New Family of the PARS Translation Systems -- MSR-MT: The Microsoft Research Machine Translation System -- The NESPOLE! Speech-to-Speech Translation System -- The KANTOO MT System: Controlled Language Checker and Lexical Maintenance Tool -- Approaches to Spoken Translation. 330 $aAMTA 2002: From Research to Real Users Ever since the showdown between Empiricists and Rationalists a decade ago at TMI 92, MT researchers have hotly pursued promising paradigms for MT, including da- driven approaches (e.g., statistical, example-based) and hybrids that integrate these with more traditional rule-based components. During the same period, commercial MT systems with standard transfer archit- tures have evolved along a parallel and almost unrelated track, increasing their cov- age (primarily through manual update of their lexicons, we assume) and achieving much broader acceptance and usage, principally through the medium of the Internet. Webpage translators have become commonplace; a number of online translation s- vices have appeared, including in their offerings both raw and postedited MT; and large corporations have been turning increasingly to MT to address the exigencies of global communication. Still, the output of the transfer-based systems employed in this expansion represents but a small drop in the ever-growing translation marketplace bucket. 410 0$aLecture Notes in Artificial Intelligence ;$v2499 606 $aNatural language processing (Computer science) 606 $aTranslation and interpretation 606 $aArtificial intelligence 606 $aMathematical logic 606 $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040 606 $aTranslation$3https://scigraph.springernature.com/ontologies/product-market-codes/N47000 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 615 0$aNatural language processing (Computer science). 615 0$aTranslation and interpretation. 615 0$aArtificial intelligence. 615 0$aMathematical logic. 615 14$aNatural Language Processing (NLP). 615 24$aTranslation. 615 24$aArtificial Intelligence. 615 24$aMathematical Logic and Formal Languages. 676 $a418/.02/0285 702 $aRichardson$b Stephen D$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465652103316 996 $aMachine Translation: From Research to Real Users$92126444 997 $aUNISA