Finite-State Computational Morphology: An Analyzer Prototype For Zulu

Abstract

As one of the largest of the 11 official languages of South Africa, Zulu is spoken by approximately 9 million people. It forms part of a language family which is characterized by rich agglutinating morphological structures. This paper discusses a prototype of a computational morphological analyzer for Zulu, built by means of the Xerox finite state tools, in particular lexc and xfst. In addition to considering both the morphotactics and the morphophonological alternation rules that apply, the focus is on implementation and other issues that need to be resolved in order to produce a useful software artefact for automated morphological analysis. The current status of the prototype is alluded to by providing morphological scope, that is the various word categories (parts of speech) that may be handled, and the lexical coverage in terms of the number of different Zulu roots that are included in the embedded lexicon of the analyzer. Preliminary testing and validation procedures are briefly discussed.

Grammar rules for the isiZulu complex verb

“…The data-based techniques, notably machine learning [27,9], have the hurdle of finding or creating a representative enough corpus and at least some rules to process them, whereas the rules-based techniques face the issue of a dearth of up-to-date, structured, grammar books, having to start afresh with formalising the grammar as grammar or regular expressions. Our literature survey indicates the latter approach is used considerably more often for Bantu languages [23,8,12,22,21]. However, use/preference does not imply more effective.…”

A Computational Approach to Zulu Verb Morphology within the Context of Lexical Semantics

“…We propose a computational approach based on ZulMorph. As a comprehensive hand-crafted finite state morphological analyser, ZulMorph not only contains lemmas of most Zulu words, based on various paper dictionaries, other language resources and text books for Zulu (Pretorius and Bosch 2003;Bosch and Pretorius 2006), but it is also arguably the most complete model of the morphological structure of Zulu words. So, when presented with a valid Zulu word, it provides the lemma as part of the full morphological analysis of the word.…”

Evaluation of the E ffects of a S pellchecker on the I ntellectuali s ation of I siZulu

“…Human language technologies for isiZulu are sparse and mostly remain in the realm of theory and academic proof-of-concept tools, such as a morphological analyser (Pretorius and Bosch, 2003), machine translation (Kotzé & Wolff 2015), search engines (Malumba et al 2015), and knowledge-to-text natural language generation (Keet & Khumalo 2017). The main drivers for end-user tools at present are large multinational companies, such as Google Inc. with its rudimentary GoogleTranslate for isiZulu and the localisation efforts of its search engine interface (at no monetary cost), and Microsoft's isiZulu localisation as a for-payment localisation extension/plugin.…”