Sujet de these: Funded PhD Research Studentship, Natural Language Processing, University of Sheffield, UK

Thierry Hamon thierry.hamon at UNIV-PARIS13.FR
Fri May 10 20:29:19 UTC 2013

Date: Wed, 8 May 2013 11:23:53 +0200
From: Leon Derczynski <leon at>
Message-ID: <CAPjwwFqMGGBbUa+w+tUS-F1mM5BvLj9i71WbndG7Z=bXP6BGPg at>

Apologies for cross-posting

Computing Veracity of Social Media Healthcare Content
University of Sheffield - Department of Computer Science

Natural Language Processing Group

3-year studentship

The NLP group at the University of Sheffield is inviting applications
for a fully funded PhD studentship on computing veracity of social media

Application closing date is 31 May 2013.

The aim of this studentship is to design natural language processing
methods to compute veracity of social media content and deal with the
specifics of medical language. The goal is to model, identify, and
verify healthcare-related misinformation and disinformation, as they
spread across online media (e.g. patient forums) and social
networks. Natural language processing (NLP) now provides many
indispensable tools for working with large unstructured text
collections, allowing effective search, information extraction and
translation. Social media content poses a number of difficult and
interesting NLP research challenges. The studentship will also involve a
close collaboration with healthcare researchers from a large NHS trust
and opportunities for working with natural language processing of
clinical records.

The PhD studentship will be associated to a larger research project
about developing novel cross-disciplinary social semantic methods,
combining document semantics, a priori large-scale world knowledge
(e.g. Linked Open Data) and a posteriori knowledge and context from
social networks, past user behaviour, and spatio-temporal metadata. The
research will also involve use of and further development of GATE
(, which is a leading open-source NLP toolkit,
developed by an established team of 12 researchers.

Candidates should have a First Class Honours or a good 2.1 degree in
Computer Science and have excellent computer programming
skills. Experience with natural language processing and machine learning
techniques is essential, and detailed knowledge of biomedical NLP,
medical ontologies, and Linked Open Data are highly desirable. A
background in linguistics and/or fluency in multiple languages would
also be desirable, but is not strictly necessary.

The grant will cover all study fees for EU and UK nationals and a living
stipend for three years plus a travel fund for attending collaborative
meetings and international conferences.

For further information please contact Dr Kalina Bontcheva
(K.Bontcheva at

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