This module aims to provide students with a comprehensive understanding of Natural Language Processing (NLP) techniques, algorithms, and applications. Students will explore the fundamental concepts and advanced techniques used in NLP, enabling them to design and develop NLP systems for a wide range of real-world applications. The module will emphasize both theoretical foundations and practical implementation aspects, equipping students with the necessary skills to tackle challenges in language processing and understanding.
A Systematically comprehend the theoretical foundations of Natural Language Processing B Apply statistical and machine learning techniques to process and analyse natural language data C Implement and evaluate different NLP algorithms and models based on performance metrics and real-world application requirements D Critically review advanced topics in NLP, such as prompt learning, language generation, and natural language understanding E Demonstrate a strong capability for undertaking individual research on NLP problems
The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments. This module will be delivered by a combination of formal lectures, seminars, and computer labs. The lectures cover the content outlined in the syllabus, providing a comprehensive understanding of natural language processing topics. Labs are designed to equip students with essential programming skills in module-related areas. Through hands-on group assignments and individual project, students gain practical experience in implementing and experimenting with natural language processing algorithms and undertaking individual research on natural language processing problems.