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Unit of study_
COMP4446: Natural Language Processing
2025 unit information
This unit introduces computational linguistics and the statistical techniques and algorithms used to automatically process natural languages (such as English or Chinese). It will review the core statistics and information theory, and the basic linguistics, required to understand statistical natural language processing (NLP). Statistical NLP is used in a wide range of applications, including information retrieval and extraction; question answering; machine translation; and classifying and clustering of documents. This unit will explore the key challenges of natural language to computational modelling, and the state of the art approaches to the key NLP sub-tasks, including tokenisation, morphological analysis, word sense representation, part-of-speech tagging, named entity recognition and other information extraction, text categorisation, phrase structure parsing and dependency parsing. You will implement many of these sub-tasks in labs and assignments. The unit will also investigate the annotation process that is central to creating training data for statistical NLP systems. You will annotate data as part of completing a real-world NLP task.
Unit details and rules
Managing faculty or University school:
Engineering
| Study level | Undergraduate |
|---|---|
| Academic unit | Computer Science |
| Credit points | 6 |
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Prerequisites:
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DATA3888 or COMP3888 or COMP3988 or CSEC3888 or SOFT3888 or ENGG3112 or SCPU3001 |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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COMP5046 |
| Assumed knowledge:
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A major in a computer science area. Knowledge of an OO programming language as covered in INFO1113 |
At the completion of this unit, you should be able to:
- LO1. apply basic linguistic knowledge to identify the structure of language
- LO2. have developed formal models to express natural language phenomenon
- LO3. have developed machine learning and deep learning for solving natural language tasks
- LO4. evaluate the performance of natural language processing systems
- LO5. implement and debug large NLP systems in a clean and structured manner
- LO6. apply machine learning/deep learning methods and information theory principles to modelling language.
Unit availability
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The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 1 2025
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Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
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