Deontic Information Extraction from Regulatory Documents at Queen’s University Belfast on FindAPhD.com

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Prof H Wang, Dr KR Rafferty No more applications being accepted Competition Funded PhD Project (Students Worldwide)

Belfast United Kingdom Electrical Engineering Electronic Engineering Computer Science

About the Project

Project Introduction:

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.

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.

Project Summary:

This PhD project will investigate deontic information extraction from regulatory documents using deep learning and natural language processing techniques.

Project Description:

Computer assisted regulatory compliance testing requires extraction of duty and obligation information from regulatory documents. This information extraction task is challenging since it requires identification and representation of special type of information from the regulatory document. Advances in artificial intelligence, especially natural language processing and deep learning and knowledge engineering, have the potential to make this information extraction process automatic. Existing studies have demonstrated the possibility, but there are still milestones to be achieved in this aspiration.

In this project we will investigate a deep learning centred approach to deontic information extraction. We will design a deep learning architecture, drawing on the state of the art in deep learning and deontic information extraction, which can learn a deontic information model. The deontic information model can then extract deontic information from regulatory documents.

Objectives:

Investigate deontic information modelling using deep learning
Investigate deontic information extraction
Conduct a case study using regulatory documents

Full-Time

Start Date: 01/10/2023

Application Closing date: 28/2/23

Funding Notes

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: View Website

Academic Requirements:

A 1st or 2.1 Hons degree or MSc with distinction in Electrical/Electronic Engineering or Computer Science with relevant technological experience.

To Apply please complete an application through the Direct Applications Portal: https://dap.qub.ac.uk/portal/user/u_login.php

References

[1] Zhengrong Guo, Gongde Guo, Hui Wang (2022. Question-answer pair generation method based on key-phrase extraction and answer filtering. Under review.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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).
[7] 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.
[8] 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).
[9] 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).

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