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COMP34411: Natural Language Systems (2012-2013)

This is an archived syllabus from 2012-2013

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
Course Leader: Allan Ramsay
Course leader: Allan Ramsay

Additional staff: view all staff
Timetable
SemesterEventLocationDayTimeGroup
Sem 1 Lecture Uni Place 6.207 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 outcomeUnit learning outcomesAssessment
A2 A5 B1Understand how to build practical NLP systems for a number of domains.
  • Examination
  • Individual coursework
A2 B3Understand how the nature of an NLP task affects the problems in building an appropriate system.
  • Individual coursework
  • Examination
B3Be 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 D4Evaluate 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: 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


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