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Semi-automated extraction of morphological grammars for Nguni with special reference to
                                    Southern Ndebele
                                                Laurette Pretorius, Sonja Bosch
                                                    University of South Africa
                                         PO Box 392, UNISA, 0003, Pretoria, South Africa
                                       E-mail: pretol@unisa.ac.za, boschse@unisa.ac.za

                                                              Abstract
A finite-state morphological grammar for Southern Ndebele, a seriously under-resourced language, has been semi-automatically
obtained from a general Nguni morphological analyser, which was bootstrapped from a mature hand-written morphological analyser
for Zulu. The results for Southern Ndebele morphological analysis, using the Nguni analyser, are surprisingly good, showing that
the Nguni languages (Zulu, Xhosa, Swati and Southern Ndebele) display significant cross-linguistic similarities that can be exploited
to accelerate documentation, resource-building and software development. The project embraces recognised best practices for the
encoding of resources to ensure sustainability, access, and easy adaptability to future formats, lingware packages and development
platforms.

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Semi-automated extraction of morphological grammars for Nguni with special reference to Southern Ndebele

  • 1. Semi-automated extraction of morphological grammars for Nguni with special reference to Southern Ndebele Laurette Pretorius, Sonja Bosch University of South Africa PO Box 392, UNISA, 0003, Pretoria, South Africa E-mail: pretol@unisa.ac.za, boschse@unisa.ac.za Abstract A finite-state morphological grammar for Southern Ndebele, a seriously under-resourced language, has been semi-automatically obtained from a general Nguni morphological analyser, which was bootstrapped from a mature hand-written morphological analyser for Zulu. The results for Southern Ndebele morphological analysis, using the Nguni analyser, are surprisingly good, showing that the Nguni languages (Zulu, Xhosa, Swati and Southern Ndebele) display significant cross-linguistic similarities that can be exploited to accelerate documentation, resource-building and software development. The project embraces recognised best practices for the encoding of resources to ensure sustainability, access, and easy adaptability to future formats, lingware packages and development platforms.