Zola Mahlaza: Classifying grammar-infused templates for ontology and model verbalisation
Channel: Lighton Phiri
Date: 2020-06-03

Zola Mahlaza gave a talk titled "Classifying Grammar-Infused Templates for Ontology and Model Verbalisation" on Tuesday, June 2, 2020, at 17H00 GMT+2. 

This talk is part of a series of seminars organised as part of the "CSC 5741: Data Mining and Warehousing" [1] postgraduate course in the Department of Computer Science [2] at The University of Zambia [3]

## Video Timeline
00:00:00 Affiliations
00:01:13 Brief Introduction
00:02:21 Presentation Outline
00:03:19 Involving Domain Experts
00:07:09 Natural Language Generation
00:14:44 Language Complexity
00:18:30 The Problem
00:20:28 Grammar-Infused Templates
00:24:32 Classification of Existing Generation Systems
00:29:28 Use Case: isiZulu Access to Semantic Web Data
00:33:35 Conclusion
00:34:11 Q&A Session

## Title of Talk

Classifying grammar-infused templates for ontology and model verbalisation

## Abstract

Natural language generation can be leveraged to generate text descriptions to assist in understanding the information captured by models and ontologies. There are various methods used for generating these kinds of texts, however, templates are a simple and popular approach. They are sometimes paired with computational grammar rules, thereby obtaining grammar-infused templates, when used to generate languages that exhibit some grammatical complexity. At the time of writing, there is no explicit specification of the types of relations that are used for pairing templates and computational grammar rules hence it is not easy to compare the suitability of the various grammar-infused templates for less-resourced languages. These languages bring grammar re-use to the fore since they it is key in reducing development efforts. In this talk, I am going to be presenting a model of extended templates that emphasises less-resourced languages, present a classification scheme based on the model, and classify existing systems that use grammar-infused templates. My classification will show that most of the systems that use the aforementioned templates are designed to infuse syntax and support detachable grammar rules, only a few tools support the embedding of word conjugation rules, and while there is variety in the supported languages, the choice of formalism used to encode the templates may limit the language permissible input language.

## Biography

Zola Mahlaza is a PhD candidate [4] and member of the knowledge engineering team [5] at the Computer Science department [6], School of Information Technology [7], University of Cape Town [8]. He received his MSc. and Bsc. (Hons.) degrees in Computer Science at the same institution. His research interests include natural language processing, knowledge representation, and conceptual modelling.

[1] http://lis.unza.zm/~lightonphiri/teaching/unza/2020/csc5741 
[2] http://www.cs.unza.zm 
[3] https://www.unza.zm 
[4] https://people.cs.uct.ac.za/~zmahlaza/
[5] http://meteck.org/keen
[6] http://cs.uct.ac.za
[7] http://www.sit.uct.ac.za
[8] http://www.uct.ac.za