Developing and analysing deep learning and natural language processing systems in the context of business information processing.

Project Introduction:

This proposed research aligns to the Advanced Research and Engineering centre within Northern Ireland. The Centre brings together expertise from PwC, University of Ulster and Queen’s University Belfast. A selection process will determine the strongest candidates across a range of projects, who may then be offered funding for their chosen project. Approximately £6000 per year is payable to the sponsored student in addition to the normal stipend. Bringing the total stipend to approximately £21,609 per annum.  

The automation of repetitive information processing tasks has the potential to realise enormous advances in productivity and user satisfaction across a range of business services and solutions. Deep learning approaches, using large scale neural network models, have recently been successfully applied to many information processing tasks, including knowledge discovery and information extraction, text summarization, and text generation. Such methods have been used to generate powerful models in the legal and commercial domain; for example, state-of-the-art Natural Language Processing models have been applied to the analysis and summarization of legal documents (Elwany et al 2019), legal textual entailment (Rosa et al 2021; Yoshioka et al 2021) and modelling the structure of commercial contracts (Hegel et al 2021). The time is therefore ripe to develop and build systems that automate textual data analysis and text generation tasks for a range of real-world, commercially orientated problems.

Project Description:

In this project, the goal is to build on recent progress in deep learning and natural language processing to develop methods and systems for processing information in commercial textual data and using the resultant representations to generate useful, task-relevant knowledge, for example, through text summarization, text generation, and question answering. Particular problems that are to be tackled within the business services domain include defining Service-Level Agreements (SLAs), a stage in the finalisation of a contract between a service provider and a client. All SLA use terminology and commonality of vocabulary that ensures the same quality of service across different units in an organisation as well as across multiple locations and subcontract work. Because of their ubiquity and importance in business services, it is costly, in terms of staff time, to ensure verification and compliance. At the same time, the structure, content and meaning of SLAs tend to follow particular patterns, and this kind of statistical regularity makes them an interesting candidate for deep learning-based text analysis, text generation, and other forms of automated processing.

A key challenge in this project is to develop models that process textual data in a way which is transparent, understandable and accountable.

In addition to the development of deep learning architectures and model explainability methods, an additional component of this project is to develop an assessment metric for the implemented intelligent systems and benchmark them against current practices. 

Project Key Words:

Artificial Intelligence, Deep Learning, Natural Language Processing, Business Services

Full-Time                

Start Date: 01 / 10 / 2022

Application Closing date: 28 / 02 / 2022

Funding Body: DfE / External Corporate Funding

Project Funding Type: funded

Funding Information:

This three year studentship, for full-time PhD study, is potentially funded by the Department for the Economy (DfE) and commences on 1 October 2022. For UK domiciled students the value of an award includes the cost of approved tuition fees as well as maintenance support (Fees £4,500 pa and Stipend rate £15,609 pa - 2022-23 rates to be confirmed). To be considered eligible for a full DfE studentship award you must have been ordinarily resident in the United Kingdom for the full three year period before the first day of the first academic year of the course.  The candidate must be ordinarily resident in Northern Ireland on the first day of the first academic year of the course, normally 1 October. For further information about eligibility criteria please refer to the DfE Postgraduate Studentship Terms and Conditions 2021-22 at https://go.qub.ac.uk/dfeterms

A selection process will determine the strongest candidates across a range of projects, who may then be offered funding for their chosen project. This is an industrially sponsored project. Approximately £6000 per year is payable to the sponsored student in addition to the stipend rate detailed above. Bringing the total stipend to approximately £21,609 per annum. 

For candidates who do not meet the above residency requirements, a small number of international studentships may be available from the School. These are expected to be highly competitive, and a selection process will determine the strongest candidates across a range of School projects, who may then be offered funding for their chosen project. 

Academic Requirements:

A minimum 2.1 honours degree or equivalent in Computer Science, Electrical and Electronic Engineering, or Psychology or relevant degree with relevant technological experience.

Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/