Enhancing Text Mining through Advanced AI and Machine Learning Techniques for Knowledge Discovery

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Alaa Mohasseb.

The work on this project could involve:

• Explore and Investigate different NLP methods to enhance the semantic understanding and context-awareness of text mining models.
• Utilize data analysis to discover hidden patterns and structures within textual datasets
• Implement different methods to capture contextualized representations of words and phrases in textual data.

Project description

Text mining plays a crucial role in extracting valuable insights from vast textual data, with the exponential growth of digital information, there is an increasing need for sophisticated techniques that can handle large-scale and diverse textual datasets.

The project aims to investigate different NLP methods to enhance the semantic understanding and context-awareness of text mining models. The project will include exploring Deep Learning Architectures to Investigate and implement different deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transfer Learning Techniques to explore the application of transfer learning, leveraging pre-trained language models such as BERT and GPT, to enhance the performance of textual models, especially in scenarios with limited labelled data.

The proposed research will contribute to the field of text mining by advancing the capabilities of AI and ML techniques, enabling more effective knowledge discovery from diverse textual datasets. The developed models and methodologies are expected to find applications in various domains, including information retrieval and sentiment analysis. The outcomes of this research are anticipated to have a significant impact on both academia and industry, fostering advancements in the field of natural language processing and knowledge discovery.

The successful candidate will be supervised by Dr Alaa Mohasseb who has extensive research experience in the field of Text Mining, Natural Language Processing and Machine learning and has been involved in several research and projects.

General admissions

The entry requirements for a PhD or MPhil include an upper second class honours degree or equivalent in a relevant subject or a master's degree in an appropriate subject. Exceptionally, equivalent professional experience and/or qualifications will be considered. All applicants are subject to interview.

If English is not your first language, you'll need English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

If you don't meet the English language requirements yet, you can achieve the level you need by successfully completing a pre-sessional English programme before you start your course.

Specific candidate requirements

Good experience in the fundamentals of Natural Language Processing, Data Analytics and Machine Learning techniques, preferably good technical skills in text processing. Competent in applying NLP toolkits, such as NLTK or Spacy, or ML toolkits such as Scikit-Learn or Tensorflow.

Good programming skills in Python, analytical skills, and knowledge of foundations of computer science are also required. You should be able to think independently, including the formulation of research problems and have strong oral and written communication skills and good time management.

How to apply

We’d encourage you to contact Dr Alaa Mohasseb (alaa.mohasseb@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.

When applying please quote project code: COMP6441025