Natural Language Processing (MSc)

Module overview

This module gives students an introduction to natural language processing (NLP) algorithms and an understanding of how to implement NLP applications.

Linked modules

prerequisites: COMP6245 or COMP6246

Aims and Objectives

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

Describe and critically appraise the different subareas of NLP
Show an appreciation of the landscape of tools used for NLP within application areas

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

The underlying algorithmic and linguistic basis for NLP techniques
Algorithms commonly used for NLP problems such as information extraction, machine translation, text summarization and question answering
Key concepts, tools and approaches for handling textual data

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

Process text corpora ready for application of NLP algorithms and techniques
Implement NLP algorithms and techniques

Learning and Teaching

Teaching and learning methods

Lectures and formative laboratories.

Study time
Type	Hours
Specialist Laboratory	8
Revision	10
Completion of assessment task	45
Wider reading or practice	45
Lecture	24
Preparation for scheduled sessions	18
Total study time	150

Resources & Reading list

Internet Resources

Speech and Language Processing, 3rd ed. draft (online preferred).

Textbooks

Aurélien Géron (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O′Reilly Media.

Steven Bird, Ewan Klein & Edward Loper (2009). Natural Language Processing with Python. O'Reilly Media.

Jurafsky and Martin (2009). Speech and Language Processing. Prentice Hall.

Assessment

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Coursework	25%
Examination	75%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Examination	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Examination	100%

Repeat Information

Repeat type: Internal & External