Module 02495 (2010)
Syllabus page 2010/2011
06-02495
Natural Language Processing 1
Level 2/I
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus
The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)
Relevant Links
Module web page
Prolog teaching pages
Outline
The module presents an overview of Natural Language Processing and its applications, followed by introductions to morphology, syntax and semantics. These topics are used to introduce some linguistic theory and appropriate algorithms for their computational implementation. Examples are mostly given using Prolog.
Aims
The aims of this module are to:
- introduce Natural Language Processing as one of the components of Artificial Intelligence, both from engineering and cognitive viewpoints
- show how Natural Language Processing techniques can be programmed using the Prolog programming language
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | describe major trends and systems in Natural Language Processing | Examination |
| 2 | define: morphology; syntax; semantics; pragmatics; and give appropriate examples to illustrate their definitions | Essay and examination |
| 3 | describe several standard methods of applying morphological and syntactic knowledge in Natural Language Processing systems, for instance: finite-state methods; probabilistic methods; context-free grammars and parsers, including the Active Chart Parsers; unification grammars and parsing | Essay and examination |
| 4 | implement context-free grammars implemented by Prolog's Definite Clause Grammar | Examination |
| 5 | describe simple feature-based semantic systems typically based on logic showing the difference between building semantic representations and interpreting semantic representations | Essay and examination |
| 6 | demonstrate a knowledge of two or more methods for resolving pronoun referents as an example of semantic interpretation | Examination |
| 7 | show an understanding of the role of pragmatics in understanding natural language | Essay and examination |
| 8 | describe an application of natural language processing (for instance machine translation) and show the place of syntactic, semantic and pragmatic processing | Examination |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
None
Co-requisites:
06-02630 (Software Workshop Prolog)
Teaching
Teaching Methods:
2 hrs/week lectures and exercise classes.
Contact Hours:
Assessment
- Sessional: 1.5 hr examination (50%), continuous assessment (50%).
- Supplementary (where allowed): By examination only.
- The nature and timing of the continuous assessment will be specified on the module web page -- see under "Relevant Links".
Recommended Books
| Title | Author(s) | Publisher, Date |
| Speech and language processing: an introduction to Natural Language Processing, Computational Linguistics and Speech Recognition (2nd ed) | Jurafsky D & Martin J H | Prentice Hall, 2008 |
| Natural language understanding (2nd ed) | Allen J | Benjamin/Cummings, 1995 |
Detailed Syllabus
- (This is copied form previous years. It is a rough guide only, as the coverage of topics this year will be significantly different.)
- Introduction - What is language and Natural Language Processing?
(1.5 weeks)
- What does it mean to know a language?
- What do we know when we know a natural language?
- What is meant by Natural Language Understanding
- Word-level descriptions (1.5 week)
- Morphology and morphological processing
- The lexicon
- Syntax and Syntactic Processors (4 weeks)
- Lexical and syntactic categories
- Introduction to the terminology of syntax and context-free grammars
- Part-of-Speech Tagging
- From Finite State Automata to Context-Free Grammars
- Definite Clause Grammar and the Very Simple Parser
- Active Chart Parsers
- Unification-based grammars and parsing
- Semantics and pragmatics (3 weeks)
- Introduction to semantics and semantic processing
- Building semantic structures
- Building more complex structures and semantic interpretation
- Discourse structure and reasoning
- Application of Natural Language Processing (1 week)
- Machine translation
Last updated: 7 Jan 2011
Source file: /internal/modules/COMSCI/2010/xml/02495.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus