Advanced Natural Language Engineering (G5114)
Advanced Natural Language Engineering
Module G5114
Module details for 2021/22.
15 credits
FHEQ Level 6
Pre-Requisite
Natural Language Engineering
Module Outline
Advanced Natural Language Engineering builds on the foundations provided by the Natural Language Engineering 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 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; 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.
Library
o Noah A. Smith (2010) Linguistic Structure Prediction, Morgan & Claypool Publishers.
o Jurafsky, D. and Martin, J. (2008) Speech and Language Processing: An Introduction to Natural Language Processing Computational Linguistics, and Speech Recognition, Prentice Hall. (Second Edition)
o Manning, C. and Schütze, H. (1999) Foundations of Statistical Natural Language Processing, MIT Press.
o Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval, Cambridge University Press.
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.
| 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 | Laboratory | 2 hours | 11111111111 |
| Spring Semester | Seminar | 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
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