Browsing by Author "Keet, Catharina"

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    Adoption of a visual model for temporal database representation
    (2016) Shunmugam, Tamindran; Keet, Catharina; Kuttel, Michelle Mary
    Today, in the world of information technology, conceptual model representation of database schemas is challenging for users both in the Software Development Life Cycle (SDLC) and the Human-Computer Interaction (HCI) domain. The primary way to resolve this issue, in both domains, is to use a model that is concise, interpretable and clear to understand, yet encompasses all of the required information to be able to clearly define the database. A temporal database is understood as a database capable of supporting reasoning of time-based data for e.g.: a temporal database can answer questions such as: - for what period was Mrs Jones single before she got married? On the other hand, an atemporal database stores data that is valid today and has no history. In the thesis, I looked at different theoretical temporal visual conceptual models proposed by temporal researchers and aimed, by means of a user-survey consisting of business users, to ascertain towards which models users a preference has. I further asked the users for firstly; whether they prefer textual or graphical representations for the entities, attributes and constraints represented by the visual models, or secondly; whether there is a preference for a specific graphical icon for the temporal entities and lastly; to ascertain if the users show a preference towards a specific theoretical temporal conceptual model. The methodology employed to reach my goal in this thesis, is one of experiments on business users with knowledge enhancements after each experiment. Users were to perform a task, and then based on analysis of the task results, they are taught additional temporal aspects so as improve their knowledge before the next experiment commences. The ultimate aim was to extract a visual conceptual model preference from business users with enhanced knowledge of temporal aspects. This is the first work done in this field and thus will aid researchers in future work, as they will have a temporal conceptual model that promotes effective communication, understandability and interpretability.
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    Computational Analyses of South African English – a Data-Driven Approach
    (2024) De Lange, Jacques; Keet, Catharina
    South African English across its multiple sub-varieties remains relatively understudied and an inclusive study of the language across the sub-varieties will enable us to uncover words and types of words unique to South African English that have been adopted or donated between the sub-varieties. This is important given South Africa's multilingual, multi-social society and the influence this has had on South African English. Such a study can also be used to improve large language models used in generative artificial intelligence, spellcheckers, sentiment analysis and speech to text technologies used in commercial applications. Computational techniques such as Part of Speech (POS) tagging, a sub-technique of Natural Language Processing, can be used to assist in understanding sentence structure and consequently, aid our uncovering of donor-adopter relationships between sub-varieties of a language. The accuracy of POS taggers on South African English therefore needs to be understood. This dissertation adopts computational data-driven approaches to studying South African English corpora to determine how accurate POS taggers are on South African English and if accuracy can be improved by creating extensions to a POS tagging model. A single layer neural network POS tagging model using word feature representations and a bidirectional long short-term memory (BLTSM) neural network POS tagging model, both trained on English are used as baseline models to predict POS tags on South African English corpora. Two modifications to the BLTSM model are then made, the first by creating a dual language model by including the Afrikaans language and the second by training the tokenizer and POS tagging neural processors of the dual language model on words unique to South African English. The evaluations show that the accuracy of the modified models is improved compared to the baseline models. The evaluation of baseline models when run on two South African English corpora shows a POS tagging F-Score of 0.69 on average across both corpora and baseline models. The evaluation of the modified models on the same corpora shows a POS tagging F-Score of 0.71 on average across both corpora and modified models. Evaluating the baseline models when run on words unique to South African English shows an average F-Score of 0.62 and evaluating the modified models when run on the same dataset shows an average F-Score of 0.72. The results demonstrate that improvements to POS tagging on South African English can be made by including Afrikaans in the model and by training this model on words unique to South African English. A novel Data-Driven Matching model is developed to investigate donor-adopter relationships in South African English. Results show that there is a commonality of use of words between South African English and Afrikaans, Sesotho and isiZulu. 15.7% of the words in the South African English corpora studied are observed to be in use in Afrikaans, 4.98% of the words are used in Sesotho and 1.09% of the words are used in isiZulu.
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    Open Access
    Developing a tool for eliciting users moral theories for automated moral agents
    (2024) Seakgwa, Kyle; Keet, Catharina
    In recent work, Rautenbach and Keet have developed a model of a system, which they name Genet, that allows the user to choose which moral theory their automated moral agent will follow. What remains unclear, however, is how the users will make this choice, given that most of them will not have the vocabulary to classify themselves in the moral philosophical terms used by Genet. This issue is what this thesis is meant to address. This was done by building three high fidelity prototypes and then conducting online user evaluations of them. Each of these prototypes implemented an algorithm that was designed based on the elicitation approach of one of three fields: cognitive science, human computer interaction and knowledge engineering. Each of these aimed to computationally determine a user's preferred moral theory, by availing itself of a human-in-the-loop component enabled by discipline specific elicitation stimuli and rules to classify the user. These prototypes were then evaluated from a usability perspective using the System Usability Scale (SUS), and from an accuracy perspective, to determine which is most validly able to elicit users' moral preferences in the form required by Genet. This latter evaluation was done using validation measures based on existing approaches to validation in moral psychology. It was observed that all the prototypes performed equally well in terms of usability, with each having an acceptable SUS score. However, each of the prototypes also performed equally inaccurately in terms of the validity of the moral theory categorizations made. While this evaluation was carried out with only a small sample size (n=20) and thus has limited generalizability, as the first study to compare and computationally implement different moral theory elicitation approaches, the present study contributes to evidence for (or at least fails to falsify) problems with the project of making the design of Automated Moral Agents dependent on elicitation of a user's one preferred moral theory. A positive claim that the data collected here does support is that, at least for some potential users, even computational elicitation tools that use empirically validated measures of moral theory preferences (like those from cognitive science) do not allow one to predict the moral judgements they will make.
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    Foundations for reusable and maintainable surface realisers for isiXhosa and isiZulu
    (2022) Mahlaza, Zola; Keet, Catharina
    Natural Language Generation (NLG) systems are used to generate text in order to reduce manual effort. Most existing systems are built to support European languages with simple and/or well-documented grammars. IsiZulu and isiXhosa, two of the largest South African languages by first language speakers, have not received a lot of attention in the field despite the potential impact of NLG systems for their speakers. The existing NLG systems created for these languages rely on ad hoc methods for surface realisation. Surface realisation is the process of generating text from a system's abstract representations of sentences. The aforementioned methods combine templates and grammar rules since the languages are low-resourced and grammatically rich. However, do not use their scant linguistic resources efficiently, they do not rely on a template specification that supports interoperability, and do not use an architecture that yields easy-to-maintain software since none exists. The objectives of this thesis are to create the foundations for easy to maintain and reusable surface realisation tools for isiXhosa and isiZulu by establishing a principled way to pair templates and grammar rules, organise surface realisation modules such that the components are modular, analysable, and reusable, and create template specifications that are interoperable. In addition, it is to demonstrate that aforementioned objectives can be achieved while generating good quality isiXhosa and isiZulu text in the data-to-text and knowledge-to-text areas. We achieve these objectives by developing a model-based approach of pairing templates and Computational Grammar Rules (CGRs) to obtain linguistically wellfounded templates that are suitable for low-resourced and grammatically rich languages. To obtain interoperable template specifications, we created a task ontology using a bottom-up approach and evaluated it via the standard practice of using Competency Questions (CQs) and removing inconsistencies via an automated reasoner. We also created an architecture that satisfies the most maintainability features from the BS ISO/IEC 25010:2011 standard. In addition, we created proof-of-concept text generation tools that use the proposed approaches and artifacts to generate isiZulu and isiXhosa text and surveyed speakers of the two languages to establish the quality of the text. We have found that most (57%) of the generated isiXhosa texts are judged positively and there is no consensus on the remaining texts, possibly due to differences in dialect. In addition, most (83%) of the generated isiZulu texts are also judged positively as they have at most one participant who considers them to be ungrammatical and unacceptable.