Saliha Muradoglu

Saliha Muradoglu

PhD Candidate
Linguistics

Qualification

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?
People: Hanna Suominen, Saliha Muradoglu
Requirements:
  • Experience with Python.
  • Strong interest and skills in linguistics, NLP, language
  • Experience in NLP/computational linguistic experience is preferable.
  • Completed coursework in Document Analysis (COMP4650) , machine learning, AI, or data science.
Background Literature:
Description: 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

Biography

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

Publications

Updated:  7 July 2017/Responsible Officer:  Director, Culture, History & Language/Page Contact:  CHL webmaster