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Deep-learning for semantic-based information extraction from natural language

Department of Computing and Information Science

About the Project

Research Group: Computing, Informatics and Applications Research Group.

Proposed supervisory team:
Dr Arooj Fatima ()
and Dr Cristina Luca ()

Theme: Semantic web, Big data deep-learning.

Summary of the research project

The idea of automatic information extraction from text documents comes from the time of first steps in natural language processing (NLP). Understanding the complex nature of natural language utterances is a key component of Artificial Intelligence. The amount of text data on the web is overwhelming and techniques to extract information automatically from this data will help manage this.

There have been various machine learning models introduced in the past to facilitate NLP tools. Recently, deep learning methods have exhibited relatively high performance in achieving certain tasks for NLP. Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains.

The proposed research will explore and utilise the methods of deep-learning to extract semantic information from the natural language text. From the semantic information, the authors mean that the system is able to (i) detect sentences and their structure (ii) identify named entities (iii) find relations and (iv) present information in a way that support human understanding. On this occasion, the authors have chosen visual representation (graph) of data as an out-put since it is relatively easier to evaluate.

Where you’ll study

Cambridge (

Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD ( In the section of the application form entitled ’Outline research proposal’, please quote the above title and include a research proposal.

Funding Notes

This project is self-funded.
Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website (View Website) as they become available.

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