This module aims to introduce the essential principles of Natural Language Processing (NLP), covering techniques, algorithms, and applications. The goal is to provide a solid foundation for understanding how computers can process and understand human language. Students will delve into both basic concepts and techniques used in NLP, gaining experience of applying NLP models and techniques to solve real-world problems.
A. Demonstrate a foundational understanding of the key concepts and techniques in natural language processing, such as tokenization, parsing, sentiment analysis, and language modeling. B. Apply statistical and machine learning techniques to process and analyze large-scale textual data, utilizing methods such as text classification, clustering, and topic modeling to extract meaningful insights. C. Implement deep learning models for NLP tasks, such as recurrent neural networks (RNNs) and transformers, and evaluate their performance using appropriate metrics, such as accuracy, precision, recall, and F1-score. D. Apply NLP models and techniques in real-world scenarios, such as chatbots, sentiment analysis, machine translation, and information retrieval. Aßnalyze the impact and challenges of deploying NLP solutions.
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, introducing 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 independent study and research on natural language processing problems.