Applied Natural Language Processing (955G5)
Applied Natural Language Processing
Module 955G5
Module details for 2025/26.
15 credits
FHEQ Level 7 (Masters)
Library
o Bird, S., Klein, E. and Loper, E. (2009) Natural Language Processing in Python.
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 Outline
Applied Natural Language Processing concerns the theory and practice of automatic text processing technologies. Topics covered on the module will include core, generic text processing models (e.g. , tokenisation, segmentation, stemming, lemmatisation, 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).
Hands-on experience with the practical aspects of this module will be gained through the weekly laboratory sessions will make extensive use of the Natural Language Toolkit which is a collection of natural language processing tools written in the Python programming language.
Module learning outcomes
Given a novel scenario in which automatic text analysis could potentially be of value, assess whether there is scope for successful deployment of NLP technology.
Design and implement a system involving generic NLP tools that is suited to a particular problem, selecting approaches that are well-suited to the specific scenario under consideration.
Formulate a clear verifiable hypothesis that forms the basis of an attempt to successfully deploy NLP technology.
Use appropriate experimental methods to reliably determine the effectiveness of an NLP software tool on actual data.
| Type | Timing | Weighting |
|---|---|---|
| Computer Based Exam | Semester 1 Assessment | 70.00% |
| Coursework | 30.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | T1 Week 7 | 100.00% |
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 | Laboratory | 2 hours | 11111111111 |
| Autumn Semester | Lecture | 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
/profiles/588726
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