Advanced Natural Language Processing
Module code: 968G5
Level 7 (Masters)
15 credits in spring semester
Teaching method: Seminar, Laboratory
Assessment modes: Coursework
Advanced Natural Language Processing builds on the foundations provided by the Applied Natural Language Processing module.
You will develop your knowledge and understanding of key topics including:
- word sense disambiguation
- vector space models of semantics
- named entity recognition
- topic modelling
- machine translation
Seminars will provide in-depth discussion of research papers related to the key topics and also general issues that arise when developing natural language processing tools, including:
- hypothesis testing
- data smoothing techniques
- domain adaptation
- generative versus discriminative learning
- semi-supervised learning
Labs will provide the opportunity for students to improve their python programming skills, experiment with some off-the-shelf technology and develop research skills.
Module learning outcomes
- Demonstrate a systematic knowledge and understanding of key challenges in the field of natural language processing (NLP) and critical awareness of current approaches to tackling these challenges.
- Critically analyse state-of-the-art NLP technologies and critically assess their application to novel problems involving large quantities of realistic data.
- Critically evaluate the effectiveness of an approach through the design and application of suitable experiments.
- Synthesise and critically assess state-of-the-art technologies for a given NLP problem based on primary scientific literature.