COM4513 Natural Language Processing
| Summary |
This module provides an introduction to the field of
computer processing of written natural language, known as
Natural Language Processing (NLP). We will cover standard
theories, models and algorithms, discuss competing
solutions to problems, describe example systems and
applications, and highlight areas of open research. Students should be aware that there are limited places available on this course. |
| Session |
Spring 2023/24 |
| Credits |
15 |
| Assessment |
Assignment and formal exam |
| Lecturer(s) |
Dr Nafise Sadat Moosavi |
| Resources |
|
| Aims |
- to give students a well-rounded feel for the problems
and approaches of Statistical Natural Language
Processing (NLP)
- to give students an understanding of the potential
areas of application of the techniques developed in
Statistical NLP
|
| Learning Outcomes |
By the end of this course the students should be able to:
- describe and discuss the subareas of NLP.
- implement NLP algorithms and
techniques.
- describe and discuss the potential and
limitations of NLP techniques for applications such as
machine translation, question answering, and information extraction
|
| Content |
Lectures will provide an overview of the field of NLP and
its sub-areas, and will introduce and explain its key
techniques, including their applicability and limitations.
In lab classes, students will practice implementing the
NLP techniques taught in class, testing their code in
application to real language data. Topics covered will
include:
- N-gram Language Modelling
- Word Classes and Part-of-Speech Tagging
- Syntactic parsing
- Neural network architectures for NLP
- Coreference resolution
- Bias in NLP
- Transfer learning
|
| Restrictions |
This module is only open to students who have taken Text Processing
(COM3110/COM4115/COM6115) and Machine Learning and Adaptive Intelligence (COM4509/6509). |
| Teaching Method |
There will be 2 formal lectures and 1 lab session per
week. |
| Feedback |
Problem sheets will be set during labs sessions and then
will discussed in labs and/or lectures.
Verbal interaction during lectures. |
|
