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Generating persuasive messages with controlled attributes for people with mental health conditions

This project is no longer listed on FindAPhD.com and may not be available.

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  • Full or part time
    Dr C Lin
    Prof E Reiter
  • Application Deadline
    Applications accepted all year round

Project Description

According to the national statistics, approximately 1 in 4 people in the UK will experience a mental health problem each year. In England, 1 in 6 people report experiencing a common mental health problem (such as anxiety and depression) in any given week. Given the prevalence of mental health problems, early identification and effective treatment are extremely important for combating the problem.

This project aims to develop data-driven approaches for generating persuasive messages with controlled attributes for people with mental health conditions. The generated messages will be used to effectively (i) promote positive attitudes toward depression in different personal and cultural contexts; and (ii) encourage people with mental health issues to seek help and treatments. In order to tackle the problem of controlled generation of messages, the computational model to be developed will be able to account for designated attributes such as potential courses of depression, gender, age group, and sentiment for the generated messages, etc. The model will also account for the dependencies of between different attributes in order to generate more effective and tailored messages. There is also a challenge of how to effectively collect training data for this project.

The student doing this project will of course need a good knowledge of computer science, as well as some knowledge of machine learning and NLP/NLG.

Candidates should have (or expect to achieve) a UK honours degree at 2.1 or above (or equivalent) in computing or related subject.

It is essential that the successful applicant has a background in Natural Language processing/generation and machine learning. Knowledge of Machine learning, statistical modelling and natural language processing/generation. Some programming or software development experience would be beneficial.

APPLICATION PROCEDURE:

• Apply for Degree of Doctor of Philosophy in Computing Science
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV
• Details of 2 academic referees

Informal inquiries can be made to Dr C Lin ([Email Address Removed]@abdn.ac.uk) with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ([Email Address Removed])

Funding Notes

This project is advertised in relation to the research areas of the discipline of Computing Science. The successful applicant will be expected to provide the funding for Tuition fees, living expenses and maintenance. Details of the cost of study can be found by visiting View Website. THERE IS NO FUNDING ATTACHED TO THESE PROJECTS.

References

Li X., van Deemter K. and Lin C. Statistics-Based Lexical Choice for NLG from Quantitative Information, The 9th International Natural Language Generation Conference (INLG), Edinburgh, UK, 2016.

Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R. and Xing, E.P., Toward controlled generation of text. arXiv preprint arXiv:1703.00955, 2017.



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