Module COMP4167: Natural Language Processing

Department: Computer Science

COMP4167: Natural Language Processing

Pre-requisites

Corequisites

Excluded Combinations

Aims

• Introduce the students to computational linguistics
• Introduce the students to Language models
• Gain experience in working with various textual data
• Gain experience in using advanced techniques to solve natural language tasks such as text parsing, understanding, classification, translation, and generation

Content

• Text Pre-processing
• Features Extraction
• Language Modelling and Neural Language Modelling
• Neural Word Embedding and their interpretation
• Recurrent Neural Networks (RNN) for Language Models
• Advanced variations of RNNs
• Sequence to Sequence Architectures
• Convolutional Neural Networks for Text Classification
• Transformers and Attention based Models
• Multitask Learning
• Natural Language Generation
• NLP Ethics and Fairness

Learning Outcomes

• On completion of the module, students will be able to demonstrate:
• Understanding of the fundamental concepts of Language Models
• Understanding of the mathematical basis of various deep-learning based language models
• Understanding of the learning algorithms behind major NLP use cases e.g. Machine Translation, Multi-task Learning, Language Generation, ...
• Understanding of the embedded bias in popular language models

• On completion of the module, students will be able to demonstrate:
• The ability to conduct independent research in the NLP field
• The ability to handle textual data and extract representative features
• The ability to use state-of-the-art NLP techniques and models to solve real-world applications

• On completion of the module, students will be able to demonstrate:
• The ability to design end-to-end solutions for real-world problems with textual input using state-of-the-art NLP techniques
• The ability to make informative decisions regarding the Deep Learning choices and the word embeddings
• Awareness of the Language Models and their biases

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Lectures enable the students to learn new material relevant to NLP concepts, word embeddings, language models, as well as their applications.
• Practical classes enable the students to put into practice learning from lectures and strengthen their understanding through application.
• Summative assessments assess the application of methods and techniques, and assess the understanding of core concepts.
• The assignment element of the coursework component consists of a coding exercise with accompanying report.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment

Example formative exercises are given during the course.