Search Results for author:
Found 26 papers, 11 papers with code
LiSTra Automatic Speech Translation: English to Lingala Case Study
In recent years there has been great interest in addressing the data scarcity of African languages and providing baseline models for different Natural Language Processing tasks (Orife et al., 2020).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Practical Approach on Implementation of WordNets for South African Languages
This paper proposes the implementation of WordNets for five South African languages, namely, Sepedi, Setswana, Tshivenda, isiZulu and isiXhosa to be added to open multilingual WordNets (OMW) on natural language toolkit (NLTK).
Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora
This paper introduces two multilingual government themed corpora in various South African languages.
Conversational Pattern Mining using Motif Detection
The subject of conversational mining has become of great interest recently due to the explosion of social and other online media.
Reinforcement Learning in Education: A Multi-Armed Bandit Approach
Advances in reinforcement learning research have demonstrated the ways in which different agent-based models can learn how to optimally perform a task within a given environment.
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
African languages are spoken by over a billion people, but are underrepresented in NLP research and development.
A Framework for Undergraduate Data Collection Strategies for Student Support Recommendation Systems in Higher Education
While much effort and detail has gone into the expansion of explaining algorithmic decision making in this context, there is still a need to develop data collection strategies Therefore, the purpose of this paper is to outline a data collection framework specific to recommender systems within this context in order to reduce collection biases, understand student characteristics, and find an ideal way to infer optimal influences on the student journey.
Comparing Synthetic Tabular Data Generation Between a Probabilistic Model and a Deep Learning Model for Education Use Cases
The ability to generate synthetic data has a variety of use cases across different domains.
Semi-supervised learning approaches for predicting South African political sentiment for local government elections
This study aims to understand the South African political context by analysing the sentiments shared on Twitter during the local government elections.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Training Cross-Lingual embeddings for Setswana and Sepedi
This paper trains Setswana and Sepedi monolingual word vectors and uses VecMap to create cross-lingual embeddings for Setswana-Sepedi in order to do a cross-lingual transfer.
An empirical investigation into audio pipeline approaches for classifying bird species
This paper is an investigation into aspects of an audio classification pipeline that will be appropriate for the monitoring of bird species on edges devices.
Is it Fake? News Disinformation Detection on South African News Websites
In this work we investigate fake news detection on South African websites.
Towards Financial Sentiment Analysis in a South African Landscape
Sentiment analysis as a sub-field of natural language processing has received increased attention in the past decade enabling organisations to more effectively manage their reputation through online media monitoring.
AI4D -- African Language Program
Advances in speech and language technologies enable tools such as voice-search, text-to-speech, speech recognition and machine translation.
1st AfricaNLP Workshop Proceedings, 2020
Proceedings of the 1st AfricaNLP Workshop held on 26th April alongside ICLR 2020, Virtual Conference, Formerly Addis Ababa Ethiopia.
Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages
Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to low-resourced languages has not yet been adequately solved.
AI4D -- African Language Dataset Challenge
This work details the organisation of the AI4D - African Language Dataset Challenge, an effort to incentivize the creation, organization and discovery of African language datasets through a competitive challenge.
Mapping the South African health landscape in response to COVID-19
When the COVID-19 disease pandemic infiltrated the world, there was an immediate need for accurate information.
Computers and Society
Extracting and categorising the reactions to COVID-19 by the South African public -- A social media study
Social Media can be used to extract discussion topics during a disaster.
Investigating similarities and differences between South African and Sierra Leonean school outcomes using Machine Learning
Available or adequate information to inform decision making for resource allocation in support of school improvement is a critical issue globally.
A Framework For Sharing Publicly Available Data To Inform The COVID-19 Outbreak in Africa: A South African Case Study
These announcements narrate the confirmed COVID-19 cases and include the age, gender, and travel history of people who have tested positive for the disease.
Computers and Society Applications
Low resource language dataset creation, curation and classification: Setswana and Sepedi -- Extended Abstract
The recent advances in Natural Language Processing have only been a boon for well represented languages, negating research in lesser known global languages.
Investigating an approach for low resource language dataset creation, curation and classification: Setswana and Sepedi
The recent advances in Natural Language Processing have been a boon for well-represented languages in terms of available curated data and research resources.
Improving short text classification through global augmentation methods
We study the effect of different approaches to text augmentation.
