Advanced Natural Language Processing (968G5)
15 credits, Level 7 (Masters)
Spring teaching
This module 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.
There will be 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
Laboratories will provide the opportunity for you to improve your Python programming skills, experiment with some off-the-shelf technology and develop research skills.
Teaching
50%: Practical (Laboratory)
50%: Seminar
Assessment
100%: Coursework (Report, Test)
Contact hours and workload
This module is approximately 150 hours of work. This breaks down into about 42 hours of contact time and about 108 hours of independent study. The 5X社区视频 may make minor variations to the contact hours for operational reasons, including timetabling requirements.
We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We鈥檙e planning to run these modules in the academic year 2024/25. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum.
We鈥檒l make sure to let you know of any material changes to modules at the earliest opportunity.