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PhD EPSRC iCASE Studentship: Building Journalistic Knowledge Graphs for Exploratory Text Analysis (iCASE - BBC)

The University of Manchester

Qualification Type: PhD
Location: Manchester
Funding for: UK Students, EU Students
Funding amount: Tuition fees plus additional stipend
Hours: Full Time
Placed On: 20th February 2019
Closes: 6th April 2019
 

Applications are invited for this fully funded (tuition fees plus additional stipend), full-time PhD EPSRC iCASE studentship, to start in September 2019 or as soon as appropriate. It is intended that this studentship is part of a newly established partnership between the University of Manchester and the BBC R&D Labs.

The Project

The interpretation of complex and large-scale textual data is an intrinsic part of the journalistic activity. Understanding multiple perspectives involved in a political debate, monitoring the evolution of the public discourse around a specific issue or collecting factual evidence, are examples of complex interpretation tasks which currently require great manual effort.

The recent evolution of Natural Language Processing (NLP) techniques brings the opportunity to automatically structure, integrate, classify and cluster textual content at scale, facilitating the analysis of complex domains of discourse. NLP can provide the fundamental infrastructure to dramatically reduce the barriers for more comprehensive, deeper and unbiased journalistic analysis.

This project is a partnership between the School of Computer Science at the University of Manchester and the BBC and it focuses on the coordination of multiple text classification and information extraction techniques for the construction of knowledge graphs which can support journalists in complex analytical tasks. The PhD student will work at the interface between NLP and Knowledge Representation, proposing and evaluating inter and intra-sentence meaning representations models to address demands of journalists in the real-world. The project will explore the application of multiple state-of-the-art machine learning techniques for discourse analysis such as story/narrative extraction, argumentation mining, opinion mining.

Applications

Applicants should have or expect to obtain a BSc 1st Class or MSc with Distinction in computer science or a related discipline and should have strong programming skills. Experience in Natural Language Processing and Machine Learning projects are highly desired. Applicants must also have British citizenship or be a permanent resident and have lived in the UK for the last 3 years. Exceptional EU students may be eligible.

Academic Supervisor: Andre Freitas (UoM, School of Computer Science)

Industrial Supervisor: Chris Newell & Andrew McParland (BBC R&D Labs)

Deadline for applications is April 6th, 2019. Qualified applicants are strongly encouraged to informally contact Andre Freitas (andre.freitas@manchester.ac.uk) or Chris Newell (chris.newell@bbc.co.uk) to discuss the application prior to applying.

Please submit a full application via the standard application route.

   
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