LEADER 05643nam 2200721Ia 450 001 9910820891403321 005 20240516205836.0 010 $a1-283-52154-7 010 $a9786613833990 010 $a90-272-7362-6 035 $a(CKB)2670000000240345 035 $a(EBL)979715 035 $a(OCoLC)804665162 035 $a(SSID)ssj0000701031 035 $a(PQKBManifestationID)12260756 035 $a(PQKBTitleCode)TC0000701031 035 $a(PQKBWorkID)10673073 035 $a(PQKB)10278338 035 $a(MiAaPQ)EBC979715 035 $a(Au-PeEL)EBL979715 035 $a(CaPaEBR)ebr10593797 035 $a(CaONFJC)MIL383399 035 $a(PPN)229783376 035 $a(EXLCZ)992670000000240345 100 $a20120423d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aChallenges for Arabic machine translation /$fedited by Abdelhadi Soudi ...[et. al.] 205 $a1st ed. 210 $aAmsterdam ;$aPhiladelphia $cJohn Benjamins Pub. Co.$d2012 215 $a1 online resource (165 p.) 225 0 $aNatural language processing ;$vv. 9 300 $aDescription based upon print version of record. 311 $a90-272-4995-4 320 $aIncludes bibliographical references and index. 327 $aChallenges for Arabic Machine Translation; Editorial page; Title page; LCC data; Table of contents; Preface; Introduction; 1. Overview; 2. Challenges of Arabic machine translation; 3. Arabic linguistic resources; 4. Example-based machine translation; 5. Statistical machine translation; 6. Knowledge-based machine translation; 7. Arabic transliteration scheme; References; Linguistic resources for Arabic machine translation; 1. Introduction; 2. LDC's distribution model; 3. Arabic speech collections; 4. Arabic text collections; 4.1 Parallel text; 4.2 NIST resources 327 $a5. Arabic morphological analyzer6. Arabic treebank and parallel English treebank; 7. Arabic-English word alignment; 8. Additional resources; References; Using morphology to improve Example-Based Machine Translation; 1. Introduction; 2. Example-Based Machine Translation: What is it and why use it?; 3. Adding morphology to EBMT for Arabic-to-English translation; 3.1 Generalization and morphological analysis in BAMA; 3.2 Phase 1: Focus on generalization and filtering; 3.2.1 Generalization; 3.2.2 Filtering; 3.2.3 Generalization and filtering are not enough 327 $a3.3 Phase 2: Generalization, filtering and adaptation3.3.1 Generalization; 3.3.2 Filtering and adaptation; 3.3.3 Scoring; 3.3.4 Results; 4. Related work; 5. Summary and conclusions; References; Using semantic equivalents for Arabic-to-English example-based translation; 1. Introduction; 2. Related work; 3. System description; 3.1 Translation corpus; 3.2 Matching; 4. Noun experiment; 4.1 The noun thesaurus; 4.2 Using noun synonyms for translation; 4.3 Experimental results; 5. Verb experiment; 5.1 The verb thesaurus; 5.2 Using synonyms in translation; 5.3 Experimental results; 6. Conclusions 327 $aReferencesArabic Preprocessing for Statistical Machine Translation; 1. Introduction; 2. Related Work; 3. Arabic Linguistic Issues; 3.1 Orthographic Ambiguity; 3.2 Clitics; 3.3 Adjustment Rules; 3.4 Templatic Inflections; 4. Preprocessing: Schemes and Techniques; 4.1 Preprocessing Techniques; 4.1.1 REGEX; 4.1.2 BAMA; 4.1.3 MADA; 4.2 Preprocessing Schemes; 4.3 Comparing Various Schemes; 5 Experiments; 5.1 Portage; 5.2 Experimental data; 5.3 Experimental Results; 5.4 Discussion; 5.5 Genre Variation; 5.6 Phrase Size; 6. Scheme Combination; 6.1 Oracle Experiment; 6.2 Rescoring-only Combination 327 $a6.3 Decoding-plus-Rescoring Combination6.4 Significance Test; 7. Conclusions; Acknowledgments; References; Preprocessing for English-to-Arabic statistical machine translation; 1. Introduction; 2. Morphological preprocessing for English-to-Arabic SMT; 2.1 Morphological segmentation of Arabic; 2.2 Recombination of segmented Arabic; 2.3 Experimental setup; 2.4 Experimental results; 3. Syntactic preprocessing for English-to-Arabic SMT; 3.1 Related work; 3.2 Reordering rules; 3.3 Experimental setup; 3.4 Results; 4. Summary; References; Lexical syntax for Arabic SMT; 1. Introduction 327 $a2. Related work 330 $aThis book is the first volume that focuses on the specific challenges of machine translation with Arabic either as source or target language. It nicely fills a gap in the literature by covering approaches that belong to the three major paradigms of machine translation: Example-based, statistical and knowledge-based. It provides broad but rigorous coverage of the methods for incorporating linguistic knowledge into empirical MT. The book brings together original and extended contributions from a group of distinguished researchers from both academia and industry. It is a welcome and much-needed r 410 0$aNatural Language Processing 606 $aMachine translating 606 $aEnglish language$xTranslating into Arabic 606 $aArabic language$xTranslating into English 606 $aSpeech processing systems 615 0$aMachine translating. 615 0$aEnglish language$xTranslating into Arabic. 615 0$aArabic language$xTranslating into English. 615 0$aSpeech processing systems. 676 $a492.7/8020285635 701 $aSoudi$b Abdelhadi$01612249 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910820891403321 996 $aChallenges for Arabic machine translation$93940938 997 $aUNINA