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Learning the morphology of Zulu with different degrees of supervision

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5 Author(s)
Sebastian Spiegler ; Department of Computer Science, University of Bristol, UK ; Bruno Golenia ; Ksenia Shalonova ; Peter Flach
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In this paper we compare different levels of supervision for learning the morphology of the indigenous South African language Zulu. After a preliminary analysis of the Zulu data used for our experiments, we concentrate on supervised, semi-supervised and unsupervised approaches comparing strengths and weaknesses of each method. The challenges we face are limited data availability and data sparsity in connection with morphological analysis of indigenous languages. At the end of the paper we draw conclusions for our future work towards a morphological analyzer for Zulu.

Published in:

Spoken Language Technology Workshop, 2008. SLT 2008. IEEE

Date of Conference:

15-19 Dec. 2008