Natural Language Engineering (G5119)
Natural Language Engineering
Module G5119
Module details for 2022/23.
15 credits
FHEQ Level 5
Module Outline
Natural Language Engineering introduces techniques and concepts involved in analysing of text by machine, with particular emphases on various practical applications that this technology drives.
Topics covered on the module will include both a variety of core, generic text processing models (e.g. , segmentation, stemming, part-of-speech tagging, named entity recognition, phrasal chunking and dependency parsing) as well as problems and application areas (e.g. document classification, information retrieval and information extraction).
We will be making extensive use of the Natural Language Toolkit which is a collection of natural language processing tools written in the
Python programming language.
Library
Bird, S., Klein, E. and Loper, E. (2009) Natural Language Processing in Python.
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)
Manning, C. and Schütze, H. (1999) Foundations of Statistical Natural Language Processing, MIT Press.
Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval, Cambridge University Press.
Module learning outcomes
Deploy generic NLP technologies to large quantities of realistic data.
Design and run an empirical investigation that would establish whether or not there is scope for successfully deploy existing text processing technologies.
Determine which language processing technologies would be effective in a given scenario.
Build a prototype system that combines off-the-shelf technologies into a practical language processing system.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 30.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | T1 Week 7 | 100.00% |
| Computer Based Exam | Semester 1 Assessment | 70.00% |
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 |
|---|---|---|---|
| Autumn Semester | Lecture | 1 hour | 22222222222 |
| Autumn 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.
Dr Jeff Mitchell
Assess convenor
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