Advanced Natural Language Engineering
Module code: G5114
15 credits in spring semester
Teaching method: Laboratory, Seminar
Assessment modes: Coursework
Advanced Natural Language Engineering builds on the foundations provided by the Natural Language Engineering 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 and machine translation.
Seminars will provide an opportunity to discuss 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 you to improve your python programming skills, experiment with some off-the-shelf technology and develop research skills.
Natural Language Engineering
Module learning outcomes
- Deploy state-of-the-art NLP technologies to novel problem involving very large quantities of realistic data.
- Use appropriate experimental methods to assesses the effectiveness of an approach in practise.
- Summarise theoretical and practical differences in various approaches to the same problem
- Select the most appropriate approaches for a given problem based on an understanding of the state-of-the-art in statistical language processing technologies.