COMP34411: Natural Language Systems (2011-2012)
This is an archived syllabus from 2011-2012
Natural Language Systems
Level: 3Credit 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 |
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A2 A5 B1 | Understand how to build practical NLP systems for a number of domains. |
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A2 B3 | Understand how the nature of an NLP task affects the problems in building an appropriate system. |
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B3 | Be able to make an informed decision, given a previously unseen practical problem, as to which NLP techniques are likely to be worthwhile. |
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A2 A5 B3 C4 D4 | Evaluate the performance of NLP systems. |
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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 ToolkitAuthor: 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