Challenges for Arabic machine translation [[electronic resource] /] / edited by Abdelhadi Soudi ...[et. al.]

• Record Nr.: UNINA9910462090303321
• Pubbl/distr/stampa: Amsterdam ; ; Philadelphia, : John Benjamins Pub. Co., 2012
• ISBN: 1-283-52154-7; 9786613833990; 90-272-7362-6
• Descrizione fisica: 1 online resource (165 p.)
• Collana: Natural language processing ; ; v. 9
• Altri autori (Persone): SoudiAbdelhadi
• Disciplina: 492.7/8020285635
• Soggetti: Machine translating; English language - Translating into Arabic; Arabic language - Translating into English; Speech processing systems; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Challenges 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; 5. 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; 3.3 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; ReferencesArabic 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; 6.3 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; 2. Related work
• Sommario/riassunto: This 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