Bit by Bit: An Illustrated History of Computers.
Bit by Bit: An Illustrated History of Computers.
A systematic comparison of various statistical alignment models
Computational Linguistics
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Arabic morphological analysis techniques: a comprehensive survey
Journal of the American Society for Information Science and Technology
Comparing example-based and statistical machine translation
Natural Language Engineering
Issues in Arabic orthography and morphology analysis
Semitic '04 Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages
Introducing Arabic sign language for mobile phones
ICCHP'10 Proceedings of the 12th international conference on Computers helping people with special needs
Enhancing readability of web documents by text augmentation for deaf people
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
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This paper describes a machine translation system that offers many deaf and hearing-impaired people the chance to access published information in Arabic by translating text into their first language, Arabic Sign Language (ArSL). The system was created under the close guidance of a team that included three deaf native signers and one ArSL interpreter. We discuss problems inherent in the design and development of such translation systems and review previous ArSL machine translation systems, which all too often demonstrate a lack of collaboration between engineers and the deaf community. We describe and explain in detail both the adapted translation approach chosen for the proposed system and the ArSL corpus that we collected for this purpose. The corpus has 203 signed sentences (with 710 distinct signs) with content restricted to the domain of instructional language as typically used in deaf education. Evaluation shows that the system produces translated sign sentences outputs with an average word error rate of 46.7% and an average position error rate of 29.4% using leave-one-out cross validation. The most frequent source of errors is missing signs in the corpus; this could be addressed in future by collecting more corpus material.