| Unit name | Natural Language Processing (Teaching Unit) |
|---|---|
| Unit code | COMS30095 |
| Credit points | 0 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Professor. Seth Bullock |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
COMS10016 Imperative and Functional Programming and COMS10018 Object Oriented Programming and Algorithms
|
| Units you must take alongside this one (co-requisite units) |
None. |
| Units you may not take alongside this one |
None. |
| School/department | School of Computer Science |
| Faculty | Faculty of Engineering |
Why is this unit important?
Natural language processing (NLP) is the set of artificial intelligence approaches and technologies involved in
extracting meaning from text and speech and driving linguistic interaction between people and artificial intelligence
systems. This unit introduces students to the design and deployment of natural language processing systems, from
text processing to syntactic and semantic parsing, information extraction and neural network architectures for NLP.
The unit will first focus on the principles of NLP before introducing different models and methods with an applied
focus. Students will explore the area through a hands-on approach in labs applying various tools and techniques to
the development of a range of NLP techniques.
How does this unit fit into your programme of study
This is an optional unit that can be taken during TB2 in Year 3 or Year 4. This allows students to build on the
knowledge that they have developed within the first 2 or 3 years of their study in learning about the state-of-the-art
in NLP.
An overview of content
This unit introduces and defines what a natural language processing (NLP) system is and exposes students to the
different approaches involved in employing them as part of artificial intelligence systems. The unit covers a range of
NLP architectures, from simple parts-of-speech taggers and parsers to the modern transformer neural network
approaches underpinning current large language models. The unit’s approach is hands-on, exploring how to build
and use NLP systems while covering the underpinning ideas and theoretical foundations.
How will students, personally, be different as a result of the unit
Students will be aware of state of the art in NLP and understand the benefits & limitations of its use, and how it can
be applied to a variety of artificial intelligence problems.
Learning Outcomes
On successful completion of this unit, students will be able to:
The unit includes lectures and a series of applied labs that allow students to learn the practical aspects of designing
and deploying NLP systems. These labs include contact time with Teaching Assistants but can also be completed
asynchronously and students are encouraged to explore the wider literature. Given the cutting-edge nature of the
content of this unit, this gives the best opportunity for students to dictate their own learning with support from
experts within the field. The unit also provides weekly coursework support sessions.
Tasks which help you learn and prepare you for summative tasks (formative):
Teaching will take place over Weeks 13-24.
The unit begins with a series of lectures which covers the theoretical background to NLP with practical labs running
alongside them once the basics have been covered. These practical labs have been designed to support the
theoretical content of the unit in a way that prepares students for the coursework assessment. Labs include
opportunities for students to discuss and check their progress on the learning outcomes of the unit. Code developed
during the labs will be a foundation of their final summative assessment.
Tasks which count towards your unit mark (summative)
Students taking this unit will be assessed by one piece of coursework, completed in groups which will assess Learning
Outcomes 1, 2, 3, 4 and 5.
M-level students are expected to go deeper in their analysis and reflection on the process and the steps followed.
When assessment does not go to plan
Students will retake relevant assessments in a like-for-like fashion.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. COMS30095).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
