COMP34411: Natural Language Systems (2011-2012)

This is an archived syllabus from 2011-2012

View 2018-2019 syllabus.

Natural Language Systems

Level: 3
Credit rating: 10
Pre-requisites: No Pre-requisites
Co-requisites: No Co-requisites
Duration: 11 weeks
Lectures: 11 x 2 hours
Examples classes: none
Lecturers: Allan Ramsay
Course lecturer: Allan Ramsay

Additional staff: view all staff

Timetable

Semester	Event	Location	Day	Time	Group
Sem 1	Lecture	LF15	Mon	11:00 - 13:00	-

Assessment Breakdown

Exam: 80%
Coursework: 20%
Lab: 0%

Themes to which this unit belongs

Natural Language, Representation and Reasoning

Aims

The course unit aims to teach the techniques required to extend the theoretical principles of computational linguistics to applications in a number of critical areas

To demonstrate how the essential components of pracftical NLP systems are built and modified.
To introduce the principal applications of NLP, including information retrieval & extraction, spoken language access to software services, and machine translation
To explain the major challenges in processing large-scale, real-world natural language
To explain the principles underlying speech recognition and synthesis, and to explore the power of 'black box' tools for these tasks
To give students an understanding of the issues involved in evaluating NLP systems

Programme outcome	Unit learning outcomes	Assessment
A2 A5 B1	Understand how to build practical NLP systems for a number of domains.	Individual coursework Examination
A2 B3	Understand how the nature of an NLP task affects the problems in building an appropriate system.	Individual coursework Examination
B3	Be able to make an informed decision, given a previously unseen practical problem, as to which NLP techniques are likely to be worthwhile.	Examination Individual coursework
A2 A5 B3 C4 D4	Evaluate the performance of NLP systems.	Examination Individual coursework

Syllabus

Introduction, motivation, review of NLP principles (1)

Large scale and robust NLP algorithms (3)

Part-of-speech tagging: probabilistic tagging, transformation-based learning

Parsing: chunking, shallow parsing, statistical parsing

Lexical semantics: lexical resources, word sense disambiguation algorithms

Infomation retrieval and extraction (2)

Document matching

Template-filling, free text question answering systems

Summarisation algorithms

Spoken language systems (3)

The nature of speech: vocal tract, acoustic analysis, the phonetics:phonology boundary, local and global phonetic contours

Speech synthesis: formant based synthesis, N-phone based synthesis (coursework 2)

Speech recognition: acoustic features, the role of linguistic constraints

Machine translation (2)

Transfer-based approaches: the MT pyramid, transfer rules

Statistical MT, memory-based MT

Reading List

Core Text

Title: Natural Language Processing with Python: analyzing text with the Natural Language Toolkit
Author: Bird, Stephen and Ewan Klein and Edward Loper
ISBN: 9780596516499
Publisher: O'Reilly
Edition: 2009 - online edition also available
Year: 2009
This book is available online at http://www.nltk.org/book

Core Text

Title: Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition (2nd edition)
Author: Jurafsky, Daniel and James H. Martin
ISBN: 9780135041963
Publisher: Pearson International
Edition:
Year: 2009