Abstract

There are many domain-specific and language-specific NLG systems, of which it may be possible to adapt to related domains and languages. The languages in the Bantu language family have their own set of features distinct from other major groups, which therefore severely limits the options to bootstrap an NLG system from existing ones. We present here our first proof-of-concept application for knowledge-to-text NLG as a plugin to the Protege 5.x ontology development system, tailored to Runyankore, a Bantu language indigenous to Uganda. It comprises a basic annotation model for linguistic information such as noun class, an implementation of existing verbalisation rules and a CFG for verbs, and a basic interface for data entry.

Highlights

  • Natural Language Generation systems require content planning and format for the selected subject domain as input and specifics about the natural language in order to generate text (Staykova, 2014), of which the latter tend to be bootstrappable for related languages (de Oliveira and Sripada, 2014)

  • There have been efforts undertaken to apply the grammar engine technique instead (Byamugisha et al, 2016a; Byamugisha et al, 2016b; Byamugisha et al, 2016c), which resulted in theoretical advances in verbalization rules for ontologies, pluralization of nouns, and verb conjugation that address the text generation needs for Runyankore

  • We present our implementation of these algorithms and required linguistic annotations as a Protege 5.x plugin

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Summary

Introduction

Natural Language Generation systems require content planning and format for the selected subject domain as input and specifics about the natural language in order to generate text (Staykova, 2014), of which the latter tend to be bootstrappable for related languages (de Oliveira and Sripada, 2014). Our NLG system uses ontologies to represent domain knowledge. The highly agglutinative structure and complex verbal morphology of Runyankore make existing NLG systems based on templates inapplicable (Keet and Khumalo, 2017). There have been efforts undertaken to apply the grammar engine technique instead (Byamugisha et al, 2016a; Byamugisha et al, 2016b; Byamugisha et al, 2016c), which resulted in theoretical advances in verbalization rules for ontologies, pluralization of nouns, and verb conjugation that address the text generation needs for Runyankore. We present our implementation of these algorithms and required linguistic annotations as a Protege 5.x plugin

Linguistic Annotations for NLG
Conclusion

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