Advanced Natural Language Processing (968G5)
Note to prospective students: this content is drawn from our database of current courses and modules. The detail does vary from year to year as our courses are constantly under review and continuously improving, but this information should give you a real flavour of what it is like to study at СÀ¶ÊÓƵ.
We’re currently reviewing teaching and assessment of our modules in light of the COVID-19 situation. We’ll publish the latest information as soon as possible.
Advanced Natural Language Processing
Module 968G5
Module details for 2024/25.
15 credits
FHEQ Level 7 (Masters)
Module Outline
Advanced Natural Language Processing builds on the foundations provided by the Applied Natural Language Processing module. Students will develop their 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 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; and 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.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | A2 Week 1 | 75.00% |
| Test | T2 Week 11 (1 hour) | 25.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Seminar | 2 hours | 11111111111 |
| Spring Semester | Laboratory | 2 hours | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Prof Julie Weeds
Assess convenor
/profiles/116624
Please note that the University will use all reasonable endeavours to deliver courses and modules in accordance with the descriptions set out here. However, the University keeps its courses and modules under review with the aim of enhancing quality. Some changes may therefore be made to the form or content of courses or modules shown as part of the normal process of curriculum management.
The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.