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Department of Computer Science


Noise-robust Natural Language Processing

Primary supervisor

Additional supervisors

  • Phong Le

Contact admissions office

Other projects with the same supervisor

Funding

  • Competition Funded Project (Students Worldwide)

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

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Project description

Natural language processing (NLP) problems are often tackled in a supervised learning manner, relying on manually annotated data which are supposed to be gold and clean.

In reality, manually annotated data can be noisy, especially when there are ambiguities between categories that only well-trained experts are able to resolve.

However employing well-trained experts to annotate data is costly, and they are not always available. The question therefore is "How can we effectively train NLP models on noisy data?".

This project aims at tackling the problem by investigating several probabilistic techniques to model noise (as latent variables, for example) and integrating them into deep neural networks.

This project is especially important to low-resource language research, to several domains such as bio-medical, construction, and law, where human annotations are difficult to have.

Person specification

For information

Essential

Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.

Desirable

Applicants will be required to evidence the following skills and qualifications.

  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
  • You will have good time management.

General

Applicants will be required to address the following.

  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Why do you believe you are suitable for doing Postgraduate Research?