Challenges for Arabic Machine Translation

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.

2. Related work.

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 Generaliz…