Abstract
The language isiZulu belongs to the Nguni group of languages, which also include isiXhosa, isiNdebele and siSwati. Of the four Nguni languages, isiZulu is the most dominant language in South Africa, which is spoken by 22.7 % of the country’s 51.8 million population. However, isiZulu (and even more so the other Nguni languages) still remains an under-resourced language for software applications. In this article we focus on controlled natural languages for structured knowledge-to-text viewed from a potential utility for verbalising business rules and OWL ontologies. IsiZulu grammar—and by extension, all Bantu languages—shows that a template-based approach is infeasible. This is due to, mainly, the noun class system, the agglutination and verb conjugation with concords for each noun class. We present verbalisation patterns for existential and universal quantification, taxonomic subsumption, axioms with simple properties, and basic cases of negation. Based on the preliminary user assessment of the patterns, selected ones are refined into algorithms for verbalisation to generate correct isiZulu sentences, which have been evaluated.
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Notes
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http://www.organic-lingua.eu; last accessed 24 December 2014.
The following abbreviations are used: A = aspect; ADV = adverb; APPL = applicative; Ext = extension; FV = final vowel; M = mood; NEG = negative tense; OC = object concord; Rad = radical; SG = singular; SC = subject concord; T = tense; VR = verb root; VS = verb stem.
http://www.w3.org/community/ontolex/wiki/Final_Model_Specification; last accessed 24 December 2014.
http://www.njas.helsinki.fi/salama/index.html; last accessed 8 December 2014.
The prefixes in these classes are identical to canonical prefixes (class 1a and 5) and are disambiguated semantically, however, the complexity is that their corresponding plurals are found in canonical classes (2a and 6 respectively).
http://en.wiktionary.org/wiki/Appendix:Zulu_nouns; last accessed 17 December 2014.
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This work is based on the research supported in part by the National Research Foundation of South Africa (CMK: Grant Number 93397).
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Keet, C.M., Khumalo, L. Toward a knowledge-to-text controlled natural language of isiZulu. Lang Resources & Evaluation 51, 131–157 (2017). https://doi.org/10.1007/s10579-016-9340-0
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DOI: https://doi.org/10.1007/s10579-016-9340-0