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PhD in Computing Science: Deep Multi-Task Learning to Extract Knowledge from Text

  • Full or part time
  • Application Deadline
    Monday, October 14, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

We are looking for a PhD student to work on next-generation algorithms for extracting knowledge from text streams (e.g. news articles of social media posts). Many online systems rely on knowledge-bases containing nuggets of information (facts or relationships between entities) about the world, which are generated through analysis of static text corpora, such as a dump of Wikipedia pages. However, over time our knowledge-base will become out-dated, as the state of the world changes, and hence we need technologies to update that knowledge base with fresh information. Deep Multi-task Models such as BERT have shown that it is possible to very effectively encode generalisable knowledge about language use within deep neural network models by co-training that model for a range of related (sub-)tasks. Hence, we are looking for a student to investigate whether can we create a general multi-task model that can accurately and reliably extract facts and relationships between entities from text streams in real-time.

The ideal candidate will have a strong background in Computer Science and either Information Retrieval or Natural Language Processing. In addition, the student is expected to have strong programming skills, some prior experience in machine learning/AI, a good command of English and team working skills.

Funding Notes

Full funding is provided for EU/UK students at standard home/EU fees and stipend rates of £15,009.

Non-EU/UK students can apply, however they would be required to pay the difference between the home/EU and international fee. The candidate must be available to start their studies before the end of December 2019.

How good is research at University of Glasgow in Computer Science and Informatics?

FTE Category A staff submitted: 41.60

Research output data provided by the Research Excellence Framework (REF)

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