This thesis describes the design and implementation of a syntax analyser and case-marker generator for selected speech acts in Arabic, named Rameses II. It is intended as a contribution to the field of natural language processing (NLP). The original motivation for this research was the fact that in one form of the Arabic writing system there are no diacritics. Diacritics are small marks placed above or below the main line of characters. It is hypothesised that I iterate users of Arabic supply these diacritics when the input text lacks them. A particularly important subset of diacritics are those associated with the final character of a word, which are called case-markers. It is these, in association with other grammatical information, that indicate the grammatical category of case. Thus, these case-markers are used in Arabic to determine the semantic roles of words in a sentence. It is the purpose of the project described in this thesis to model computationally the process whereby these case-markers are assigned. The Rameses II system is implemented in Prolog. It parses a small but substantial portion of Arabic syntax, speech divided namely twelve of the nineteen classes of act. Arabic sentences are traditionally into declaratives (which are sentences that accept a true or false evaluation) and speech acts (which do not). Because there are already in existence substantial morphological analysers for Arabic, Rameses II assumes an input that has already been analysed morphologically. Thus its main roles are ( 1 ) to parse this input string and ( 2 ) to generate case-markers. Such a generator will be a necessary component in future holistic systems for understanding Arabic. Speech acts are a significant and well-defined area of Arabic grammar, and many aspects of the treatment suggested here could readily be extended to other parts of the language. As its underlying linguistic model of how Arabic grammar works, the system uses systemic grammar. This is a semantically motivated model of language which, as far as I can discover, has not so far been used for the description of Arabic. However, it has been widely used for English and many other languages, and has a rapidly growing use in NLP. This thesis therefore makes a contribution the linguistic description of Arabic, as wei I as the field of NLP. The main body of the thesis is concerned with ( 1 ) current work in the field of natural language processing, (2) the Arabic language, and (3) an indepth discussion of the implemented system, including its architecture, operation, and development. I t concludes with a brief evaluation and suggestions for applications. Thus, this research has successfully applied a new linguistic model to Arabic, resulting in the first automated system for the syntactic analysis of speech acts and the generation of case-markers for that language.

This dissertation investigates the challenges that Deaf adults encounter at the task of learning computer literacy skills. Deaf adults who communicate using South African Sign Language (SASL) come from poor socio-economic backgrounds are not familiar with the written form of English. They rely on interpreters and Deaf teachers to translate written text into SASL for them to learn computer literacy skill. We present our theme of support, in which Deaf people learn via an intermediary, a teacher or facilitator, in intermediated supported learning. We propose a shift from intermediated supported learning to multimedia supported learning which is most appropriate for the context. Using Community-based co-design we implement two systems: an authoring tool to support lesson content creation by the teacher and a mobile prototype that uses sign language videos to provide computer literacy instruction. We evaluate the two systems to evaluate if they support multimedia-supported learning. The authoring tool allowed the facilitator to create tailored lessons for the Deaf learners using pre-recorded SASL videos and images. The Deaf learners demonstrated ability to do self-paced learning while using the mobile system, better suited to Deaf learners with basic exposure to computer literacy skills.

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published_or_final_version / Computer Science / Master / Master of Philosophy

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