Module 06-02495 (2017)
Natural Language Processing 1
|Mark Lee||Semester 1||10 credits|
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.
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
- provide foundations for the programming of Natural Language Processing techniques.
On successful completion of this module, the student should be able to:
- Describe major concepts, trends, approaches/systems, and difficulties in Natural Language Processing and the study of language generally.
- Discuss and illustrate the potential distinctions between morphology, syntax, semantics and pragmatics
- 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; dependency parsing.
- Describe the basics of symbolic and statistical semantic algorithms.
- Demonstrate knowledge of at least one method for a task such as pronoun reference resolution, coreference resolution, or named-entity recognition as an example of a specific, core task in interpretation.
- Describe an application of natural language processing (for instance machine translation or document summarization) and show the place of syntactic, semantic and pragmatic processing.
2 hrs/week lectures and exercise classes.
Contact Hours: 23
Sessional: 1.5 hr examination (80%), continuous assessment (20%).
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".
- Introduction and Overview
- Nature of language, words, word classes, lexical ambiguity, morphology, etc.
- Part-of-speech tagging.
- Grammars and parsing.
- Semantics: symbolic and statistical approaches.
- Pragmatics, cohesion, discourse structure.
- Areas of special difficulty such as idioms, figurative language, speech acts, textese.
- Applications (may be interspersed through above topics).
Programmes containing this module
- BSc Artificial Intelligence & Computer Science 
- BSc Artificial Intelligence & Computer Science with an Industrial Year 
- BSc Artificial Intelligence & Computer Science with Study Abroad [452B]
- BSc Computer Science 
- BSc Computer Science with an Industrial Year 
- BSc Computer Science with Study Abroad 
- BSc Year in Computer Science 
- MSci Computer Science 
- MSci Computer Science with an Industrial Year 
- MSci Computer Science with Study Abroad