Module 11223 (2012)
Syllabus page 2012/2013
Natural Language Processing & Applications
The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)
Changes and updates
This module is not offered in 2012/2013
The module will cover: levels of NLP, speech (phonetics, phonology); grammar (morphology, syntax); meaning (semantics, pragmatics); applications (text-to-speech, speech-to-text, parsing, MT, NL interfaces). The emphasis will be on the background needed to understand practical applications of speech and natural language processing.
The aims of this module are to:
- introduce students to the basics of natural language processing (including speech), with an emphasis on its practical applications
- cover enough of the linguistic and computing background to allow understanding and evaluation of some applications, such as speech synthesis, speech recognition or machine translation
|On successful completion of this module, the student should be able to:||Assessed by:|
|1||show knowledge and understanding of the core theory underlying speech and natural language processing||Continuous Assessment, Examination|
|2||explain its relevance to specific applications||Continuous Assessment, Examination|
|3||show an understanding of some current applications of NLP, and evaluate them, demonstrating an appreciation of their strengths and weaknesses||Continuous Assessment, Examination|
Knowledge of a programming language is assumed. Neither 06-02495 (Natural Language Processing 1) nor
02630 are prerequisites, although there will be a small amount of common material. You will need to be willing to grapple with the complexities of natural language, including learning some basic phonetics and linguistics. A knowledge of another language can help, although it isn't essential.
2 hrs lectures per week plus 6 labs/tutorials
- Sessional: 1.5 hr examination (80%), continuous assessment (20%).
- Supplementary (where allowed): By examination only (100%)
- The continuous assessment will consist of an essay or a programming project.
|Speech and Language Processing||Jurafsky, D. & Martin, J.H.||Prentice Hall, 2000|
|Statistical Language Learning||Charniak, E.||Cambridge: MIT Press, 1993|
|Fuondations of Statistical Natural Language Processing||Manning, C. & Schütze, H.||Cambridge: MIT Press, 1999|
- Introduction. What is 'natural language'? Definitions, 'levels' of language processing, dialects and language changes. 1 week.
- Phonetics & phonology. Linguistic concepts -- phonemes, allophones, feature sets, phonological rules. The phonemes of English ('Standard English English' and 'Standard American English') and their representation in the IPA. Applications in speech synthesis (TTS) and speech recognition (STT). Whole word, grapheme-phoneme-allophone and biphone synthesis techniques. Language models in speech recognition. Introduction to further issues, including stress and intonation. 3 weeks.
- Morphology. Introduction to morphology in both spoken and written language. Definitions -- morpheme, inflectional and derivational morphology. Applications, including spelling checkers. 1 week.
- Syntax. Brief overview of the grammar of English (noun phrase, verb phrase, sentence). Outline of Phase-Structure Grammars using Prolog notation, approaches to generation and parsing (not algorithms). Syntax trees. Application to Machine Translation. 3 weeks.
- Meaning. Semantic features and their applications. 'Case' / thematic roles. The limitations of current approaches to semantic processing -- anaphora, ellipsis, etc. Brief introduction to pragmatics. 3 weeks.
Last updated: 9 Jul 2009
Source file: /internal/modules/COMSCI/2012/xml/11223.xml