CSU22062 – Natural Language Processing

Module Code	CSU22062
Module Name	Natural Language Processing
ECTS Weighting[1]	5 ECTS
Semester taught	Semester 2
Module Coordinator/s	Martin Emms

Module Learning Outcomes

On successful completion of this module, students will be able to:

LO1 understand and work with implementations of Finite State Automata and regular languages appreciating both their strengths and weakness and the areas of language processing to which they might be applied
LO2 understand and work with implementations of context-free grammars and parsers, including stack-based and chart parsers.
LO3 understand and work with implementations of probabilistic methods in language processing such as statistical parsers, the use of Hidden Markov Models for speech recognition or statistical machine translation
LO4 understand the uses to which Feature Structures may be put in grammars of natural languages
LO5 understand some aspects of recursive computations on grammatical structures to serve semantic ends

Module Content

Regular languages
i. notion of finite state automaton and transducer and areas of
application
ii. properties and limitations of finite state methods – centreembedding
iii. C++ implementation of finite state automata
Context Free languages

i. illustration of applications to natural language and potential
limitations – crossed dependencies
ii. bottom-up and top-down stack-based parsers, including
backtracking. chart-based parsers. Properties of these parsers and
their implementation in C++
iii. long-distance dependencies and slash-grammars

3 Feature structures
i. untyped and typed features structures with their associated
unification algorithms and areas of possible application in language
description
ii. C++ implementation via the LilFes library

4 Brief into to Probailistic Methods in NLP, topic varying year to year,
examples being the use of Hidden Markov models in speech recognition, or
statistical machine translation

5 Brief into recursive computation of semantic values from grammatical structures

Teaching and learning Methods

There is a mixture of lectures, lab sessions and tutorials. Most frequently there will
be a 2 lectures and one lab-session per week, but there will be occasions where 1 or
more of the time-tabled lecture sessions will actually be a lab-session or a tutorial.

There will be many exercises in online materials, all of which students
will be encouraged to attempt; a subset of these will be set as assignments and
graded. To all of the exercises suggested answers will be provided some time after
the exercise has been first made available

Assessment Details

Content

Assessment Component	Brief Description	Learning Outcomes Addressed	% of total	Week set	Week Due
Examination	In Person Exam	L01 – L05	60
Coursework 1	FSAs	L01	10	2
Coursework 2	Writing CFGs	L02	8	4
Coursework 3	Parsers	L02	14	6
Coursework 4	Semantics	L05	8	10

The breakdown of Coursework into individual assignments summarises the previous year; it should be treated as an indicator of what will happen in this year rather than an exact schedule

Reassessment Details

In Person Exam

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by:	33 hours
Lecture	22 hours
Laboratory	11 hours
Tutorial or seminar	0 hours
Other	0 hours
Independent study (outside scheduled contact hours), broken down by:	69 hours
Preparation for classes and review of material (including preparation for examination, if applicable	33 hours
completion of assessments (including examination, if applicable)	36 hours
Total Hours	102 hours

Module Pre-requisites

Prerequisite modules: NA

Other/alternative non-module prerequisites: NA

Module Co-requisites

None