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School of Engineering and Informatics (for staff and students)

Natural Language Engineering (G5119)

Natural Language Engineering

Module G5119

Module details for 2021/22.

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.

TypeTimingWeighting
Coursework30.00%
Coursework components. Weighted as shown below.
ReportT1 Week 7 100.00%
Computer Based ExamSemester 1 Assessment70.00%
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
Autumn SemesterLecture1 hour22222222222
Autumn SemesterLaboratory2 hours11111111111

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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School of Engineering and Informatics (for staff and students)

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

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