Developing and analysing document understanding and natural language processing systems in the context of business information extraction
Applications are now CLOSED
This proposed research aligns to the new Advanced Research and Engineering centre (ARC) within Northern Ireland. This Centre will drive future innovations in technology and enhance our capabilities in important research areas such as robotic process automation (RPA), workflow automation, visualisation, data analytics and artificial intelligence (AI). The Centre brings together expertise from PwC, University of Ulster and Queen’s University Belfast. This research project aligns to the workflow and AI streams within the Centre. 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. 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). Moreover, document image understanding models have shown promising utility in extracting relevant information from structured commercial documents. 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.
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 document data and using the resultant representations to generate useful, task-relevant knowledge, for example, through text summarization, text generation, and question answering. The project will build on modern foundational models and architectures in machine learning, and in particular transformer models, such as BERT (Devlin et al 2019) and RoBERTa (Liu et al 2019). 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. They are defined at different levels and used by organisations internally as well as in the supplier/customer relationship. 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. The large-scale transformer models that are now ubiquitous in Natural Language Processing research are essentially “black boxes”, where the representations of linguistic meaning and the basis for output decisions by the model are not readily explainable to an end user. Currently, therefore, there is intensive research on developing a wide range of statistical methods and other analysis techniques to better quantify and explain such models (Rogers et al 2020). For example, a key consideration is identifying key components in a document (sentences or words) that are critically relevant to the task at hand (Ormerod et al 2019).
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. In particular, this will involve evaluating error rates and making recommendations for the depth of deployment of intelligent agents in practice.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Elwany, E., Moore, D., & Oberoi, G. (2019). Bert goes to law school: Quantifying the competitive advantage of access to large legal corpora in contract understanding. arXiv preprint arXiv:1911.00473.
Hegel, A., Shah, M., Peaslee, G., Roof, B., & Elwany, E. (2021). The Law of Large Documents: Understanding the Structure of Legal Contracts Using Visual Cues. arXiv preprint arXiv:2107.08128.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
Ormerod, M., Martínez-del-Rincón, J., Robertson, N., McGuinness, B., & Devereux, B. (2019, August). Analysing representations of memory impairment in a clinical notes classification model. In Proceedings of the 18th BioNLP Workshop and Shared Task (pp. 48-57).
Rogers, A., Kovaleva, O., & Rumshisky, A. (2020). A primer in bertology: What we know about how bert works. Transactions of the Association for Computational Linguistics, 8, 842-866.
Rosa, G. M., Rodrigues, R. C., de Alencar Lotufo, R., & Nogueira, R. (2021, June). To tune or not to tune? zero-shot models for legal case entailment. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law (pp. 295-300).
Yoshioka, M., Aoki, Y., & Suzuki, Y. (2021, June). BERT-based ensemble methods with data augmentation for legal textual entailment in COLIEE statute law task. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law (pp. 278-284).
*Please note that the deadline for applications from international candidates closed on 28 February*
International: We welcome applications from international candidates. For candidates who do not meet the DfE funding residency requirements, a small number of international studentships may be available from the School. These are awarded via a competitive, selection process which will determine the strongest candidates across a range of School projects, who may then be offered funding for their chosen project.
UK/ROI: Applications from candidates in the UK and ROI are eligible for consideration for a DfE Studentship. As this is an industry-sponsored PhD, approximately £6,000 per year is payable to the sponsored student in addition to the annual DfE stipend if successful. Full eligibility information for UK/ROI candidates can be viewed via: https://www.economy-ni.gov.uk/publications/student-finance-postgraduate-studentships-terms-and-conditions
The minimum academic requirement for admission is normally an Upper Second Class Honours degree from a UK or ROI Higher Education provider in a relevant discipline, or an equivalent qualification acceptable to the University.
Full-time: 3 Years
Computer Science overview
The School of Electronics, Electrical Engineering and Computer Science (EEECS) aims to enhance the way we use technology in communication, data science, computing systems, cyber security, power electronics, intelligent control, and many related areas.
You’ll be part of a dynamic doctoral research environment and will study alongside students from over 40 countries world wide; we supervise students undertaking research in key areas of computer science, including: computing systems, artificial intelligence and cybersecurity. As part of a lively community of over 100 full-time and part-time research students you’ll have the opportunity to develop your research potential in a vibrant research community that prioritises the cross-fertilisation of ideas and innovation in the advancement of knowledge.
Within the School we have a number of specialist research centres including a Global Research Institute, the Institute of Electronics, Communications and Information Technology (ECIT) specialising in Cyber Security, Wireless Innovation and Data Science and scalable computing.
Many PhD studentships attract scholarships and top-up supplements. PhD programmes provide our students with the opportunity to acquire an extensive training in research techniques.
Computer Science Highlights
- ECIT brings together, in one building, internationally recognised research groups specialising in key areas of advanced digital and communications technology.
- Queen’s researchers have strong links with the local industry, which boasts a rich mix of local startups and multi-nationals. Belfast is the second fastest growing region in the UK in terms of Knowledge Economy activity (Northern Ireland Economy Report, 2018).
- CSIT brings together research specialists in complementary fields such as data security, network security systems, wireless-enabled security systems, intelligent surveillance systems; and serves as the national point of reference for knowledge transfer in these areas.
World Class Facilities
- The state-of-the-art £14m Computer Science Building and the Institute of Electronics, Communications and Information Technology offer bespoke research environments.
The Institute of Electronics, Communications and Information Technology (ECIT), with state-of-the-art technology, offers a bespoke research environment.
Internationally Renowned Experts
- You will be working under the supervision of leading international academic experts.
Research students are encouraged to play a full and active role in relation to the wide range of research activities undertaken within the School and there are many resources available including:
- A wide range of personal development and specialist training courses offered through the Personal Development Programme
- Access to the Queen's University Postgraduate Researcher Development Programme
- Office accommodation with access to computing facilities and support to attend conferences for full-time PhD students
To do a PhD was one of the most challenging but rewarding decisions I have taken. While having a PhD was helpful in the job market, the real benefit was in stretching my mind and deepening my thinking. This is proving particularly useful as I head up a new local R&D team which has to stay ahead of the game by exploiting the latest research.
R&D Team Leader, Mintel
Research within the School is organised into research themes combining strengths by working together on major projects, in many cases in collaboration with key technology companies.
ECIT brings together internationally recognised research groups specialising in key areas of advanced digital and communications technology.
PhD Opportunities are available in a wide range of computer science subjects, aligned to the specific expertise of our PhD supervisors.
Queen’s is a leader in commercial impact and one of the five highest performing universities in the UK for intellectual property commercialisation. We have created over 80 spin-out companies. Three of these -
Kainos, Andor Technology and Fusion Antibodies - have been publicly listed on the London Stock Exchange.
Queen’s has strong collaborative links with industry in Northern Ireland, and internationally. It has a strong funding track record with EPSRC and the EC H2020 programme.
The research profile produced by the 2014 UK Research Excellence Framework (REF) graded 80 per cent of our research activity as 'world-leading' or 'internationally excellent', confirming the School's reputation as an internationally-leading department.
For further information on career opportunities at PhD level please contact the Faculty of Engineering and Physical Sciences Student Recruitment Team on askEPS@qub.ac.uk. Our advisors - in consultation with the School - will be happy to provide further information on your research area, possible career prospects and your research application.
People teaching you
Course structureThere is no specific course content as such. You are expected to take research training modules that are supported by the School which focus on quantitative and qualitative research methods. You are also expected to carry out your research under the guidance of your supervisor.
Over the course of study you can attend postgraduate skills training organised by the Graduate School.
You will normally register, in the first instance, as an ‘undifferentiated PhD student’ which means that you have satisfied staff that you are capable of undertaking a research degree. The decision as to whether you should undertake a PhD is delayed until you have completed ‘differentiation’.
Differentiation takes place about 8-9 months after registration for full time students and about 16-18 months for part time students: You are normally asked to submit work to a panel of up two academics and this is followed up with a formal meeting with the ‘Differentiation Panel’. The Panel then make a judgement about your capacity to continue with your study. Sometimes students are advised to revise their research objectives or to consider submitting their work for an MPhil qualification rather than a doctoral qualification.
To complete with a doctoral qualification you will be required to submit a thesis of approx 80,000 words and you will be required to attend a viva voce [oral examination] with an external and internal examiner to defend your thesis.
A PhD programme runs for 3-4 years full-time or 6-8 years part-time. Students can apply for a writing up year should it be required.
The PhD is open to both full and part time candidates and is often a useful preparation for a career within academia or consultancy.
Full time students are often attracted to research degree programmes because they offer an opportunity to pursue in some depth an area of academic interest.
The part time research degree is an exciting option for professionals already working in the education field who are seeking to extend their knowledge on an issue of professional interest. Often part time candidates choose to research an area that is related to their professional responsibilities.
If you meet the Entry Requirements, the next step is to check whether we can supervise research in your chosen area. We only take students to whom we can offer expert research supervision from one of our academic staff. Therefore, your research question needs to engage with the research interests of one of our staff.
- Assessment processes for the Research Degree differ from taught degrees. Students will be expected to present write up their work at regular intervals to their supervisor who will provide written and oral feedback; a formal assessment process takes place annually.
This Annual Progress Review requires students to present their work in writing and orally to a panel of academics from within the School. Successful completion of this process will allow students to register for the next academic year.
The final assessment of the doctoral degree is both oral and written. Students will submit their thesis to an internal and external examining team who will review the written thesis before inviting the student to orally defend their work at a Viva Voce.
- Supervisors will offer feedback on the research work at regular intervals throughout the period of registration on the degree.
Full time PhD students will have access to a shared office space and access to a desk with personal computer and internet access.
The minimum academic requirement for admission to a research degree programme is normally an Upper Second Class Honours degree from a UK or ROI HE provider, or an equivalent qualification acceptable to the University. Further information can be obtained by contacting the School.
For information on international qualification equivalents, please check the specific information for your country.
English Language Requirements
Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component or equivalent qualification acceptable to the University is required (*taken within the last 2 years).
International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.
For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
|Northern Ireland (NI) 1||£4,596|
|Republic of Ireland (ROI) 2||£4,596|
|England, Scotland or Wales (GB) 1||£4,596|
|EU Other 3||£23,850|
1 EU citizens in the EU Settlement Scheme, with settled or pre-settled status, are expected to be charged the NI or GB tuition fee based on where they are ordinarily resident, however this is provisional and subject to the publication of the Northern Ireland Assembly Student Fees Regulations. Students who are ROI nationals resident in GB are expected to be charged the GB fee, however this is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.
2 It is expected that EU students who are ROI nationals resident in ROI will be eligible for NI tuition fees. The tuition fee set out above is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.
3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.
All tuition fees quoted are for the academic year 2021-22, and relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.
More information on postgraduate tuition fees.
Computer Science costs
There are no specific additional course costs associated with this programme.
Additional course costs
Depending on the programme of study, there may also be other extra costs which are not covered by tuition fees, which students will need to consider when planning their studies . Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £100 per year for photocopying, memory sticks and printing charges. Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen. There are also additional charges for graduation ceremonies, and library fines. In undertaking a research project students may incur costs associated with transport and/or materials, and there will also be additional costs for printing and binding the thesis. There may also be individually tailored research project expenses and students should consult directly with the School for further information.
Some research programmes incur an additional annual charge on top of the tuition fees, often referred to as a bench fee. Bench fees are charged when a programme (or a specific project) incurs extra costs such as those involved with specialist laboratory or field work. If you are required to pay bench fees they will be detailed on your offer letter. If you have any questions about Bench Fees these should be raised with your School at the application stage. Please note that, if you are being funded you will need to ensure your sponsor is aware of and has agreed to fund these additional costs before accepting your place.
How do I fund my study?1.PhD Opportunities
Find PhD opportunities and funded studentships by subject area.2.Funded Doctoral Training Programmes
We offer numerous opportunities for funded doctoral study in a world-class research environment. Our centres and partnerships, aim to seek out and nurture outstanding postgraduate research students, and provide targeted training and skills development.3.PhD loans
The Government offers doctoral loans of up to £26,445 for PhDs and equivalent postgraduate research programmes for English- or Welsh-resident UK and EU students.4.International Scholarships
Information on Postgraduate Research scholarships for international students.
Funding and Scholarships
The Funding & Scholarship Finder helps prospective and current students find funding to help cover costs towards a whole range of study related expenses.
How to Apply
Apply using our online Postgraduate Applications Portal and follow the step-by-step instructions on how to apply.
Find a supervisor
If you're interested in a particular project, we suggest you contact the relevant academic before you apply, to introduce yourself and ask questions.
To find a potential supervisor aligned with your area of interest, or if you are unsure of who to contact, look through the staff profiles linked here.
You might be asked to provide a short outline of your proposal to help us identify potential supervisors.