Undergraduate Course: Natural Language Understanding, Generation, and Machine Translation (INFR11157)
This course will be closed from 31 July 2025
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Following the closure of this course, a suggested replacement for students to consider is: Advanced Topics in Natural Language Processing INFR11287.
This course explores current research on processing natural language: interpreting, generating, and translating. The course will focus mainly on deep learning approaches to various NLP tasks and applications. It will provide an introduction to the main neural network architectures used in NLP and how they are used for tasks such as syntactic and semantic parsing, as well as end-user applications such as machine translation and text summarisation.
Building on linguistic and algorithmic knowledge taught in prerequisite courses, this course also aims to further develop students' understanding of the strengths and weaknesses of current approaches with respect to linguistic and computational considerations. Practical assignments will provide the opportunity to implement and analyse some of the approaches considered. |
| Course description |
The course aims to familiarise students with recent research across a range of topics within NLP, mainly within the framework of neural network models, and with a focus on applications such as machine translation, summarisation, and semantic parsing.
|
Information for Visiting Students
| Pre-requisites | As above. |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- identify and discuss the main linguistic, machine learning, and ethical challenges involved in the development and use of natural language processing systems
- understand and describe state-of-the-art models and algorithms used to address challenges in natural language processing systems
- design, implement, and apply modifications to state-of-the-art natural language processing systems
- understand the computational and engineering challenges that arise in the use of different models for natural language processing, and discuss the pros and cons of different models for a given task
- understand, design and justify approaches to evaluation and error analysis in natural language processing systems
|
Reading List
| There is no textbook for the course; readings will come from recent research literature. |
Additional Information
| Course URL |
https://opencourse.inf.ed.ac.uk/nlu-11 |
| Graduate Attributes and Skills |
Students will develop their skills in reading research papers and identifying pros and cons of different approaches. They will also learn to analyse and discuss results from their own implementations. |
| Keywords | Natural Language Processing,NLU+,Machine Translation |
Contacts
| Course organiser | Dr Alexandra Birch-Mayne
Tel: (0131 6)50 8286
Email: a.birch@ed.ac.uk |
Course secretary | Miss Kerry Fernie
Tel: (0131 6)50 5194
Email: kerry.fernie@ed.ac.uk |
|
|
University Homepage