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School of Engineering and Informatics (for staff and students)

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.

TypeTimingWeighting
Coursework100.00%
Coursework components. Weighted as shown below.
ReportA2 Week 1 75.00%
TestT2 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.

TermMethodDurationWeek pattern
Spring SemesterLaboratory2 hours11111111111
Spring SemesterSeminar2 hours11111111111

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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School of Engineering and Informatics (for staff and students)

School Office:
School of Engineering and Informatics, ÄûÃÊÊÓƵ, Chichester 1 Room 002, Falmer, Brighton, BN1 9QJ
ei@sussex.ac.uk
T 01273 (67) 8195

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