Saliha Muradoglu

Saliha Muradoglu

PhD Candidate


BSc(Hons), MA
Contact details
Room: 1.233
Building: HC Coombs Building

Available Project

Ablation study: What level of linguistic detail is needed for word-level modelling?

<td>As NLP (Natural language processing) tools are expanded to include new languages one of the big bottlenecks is labelled data availability. This issue is particularly acute for low-resource languages. So the question of annotation detail and quality is important. How much detail is needed for supervised learning? Is there a minimum number of labels to capture linguistic patterns?

In this project, you will explore the importance of labels/tags for word-level modelling (morphology and phonology) by performing an ablation study. You will be training several ML (machine learning) models for word-level phenomenon and contextualise your study findings in the body of existing literature. You may even choose to utilise information theoretic metrics to quantify the informativeness of each tag. Keywords: machine learning, natural language processing, computational linguistics, language


Saliha is currently a PhD Candidate at the School of Culture, History and Language.

Research Interests

Computational linguistics, computational morphology, computational phonology, NLP, computational modelling, cognitive modelling, information theory, morphological learning, low-resource languages, language documentation

Research Outputs


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