- UCAS course code
- QL13
- UCAS institution code
- M20
Bachelor of Arts (BA)
BA Linguistics and Sociology
- Typical A-level offer: ABB
- Typical contextual A-level offer: BBC
- Refugee/care-experienced offer: BBC
- Typical International Baccalaureate offer: 34 points overall with 6,6,5 at HL
Fees and funding
Fees
Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £26,500 per annum. For general information please see the undergraduate finance pages.
Policy on additional costs
All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).
Scholarships/sponsorships
Scholarships and bursaries are available to eligible Home/EU students, this is in addition to the government package of maintenance grants.
- Find out more from Student Finance
- International student? Check your country page
- Interested in a student internship?
Course unit details:
Computational Linguistics
Unit code | LELA32051 |
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Credit rating | 20 |
Unit level | Level 3 |
Teaching period(s) | Semester 1 |
Offered by | Linguistics & English Language |
Available as a free choice unit? | Yes |
Overview
The last two decades have seen an explosion in the use of language technologies - from consumer applications such as Alexa and Google Translate to the behind-the-scenes use by, for example, social media, news and marketing companies. This course unit will provide an introduction to the field of computer natural language processing (NLP). It will focus on technologies for representing word meaning, performing syntactic analysis of sentences, composing sentence meanings, translating between languages and conducting human-machine conversation. We will consider ways in which linguistic theory is useful in performing each of these tasks, and conversely how decades of experience in building such systems can inform linguistic theory. Students will acquire a basic knowledge of the Python programming language, and gain experience of building the kinds of models that are deployed in real-world technologies. No prior programming experience is required.
Pre/co-requisites
Unit title | Unit code | Requirement type | Description |
---|---|---|---|
English Word and Sentence Structure | LELA10301 | Pre-Requisite | Recommended |
A foundational unit in morphology and syntax, e.g. LELA10301 English Word and Sentence Structure is recommended.
Aims
The principal aims of the course unit are to:
- Familiarize students with different approaches to the computer processing of human language
- Enable students to decide which technologies to apply to novel NLP challenges
- Give students experience of building, running and interpreting the performance of programs
- Encourage students to apply insights gained from the computer processing of natural language to their analysis of linguistic data and development of linguistic theory
Learning outcomes
Knowledge and understanding
Students who successfully complete this course will acquire an understanding of:
- Probabilistic approaches to language
- Two core types of machine learning (supervised learning, unsupervised learning)
- Five key areas of NLP (vector space and embedding representations of word meaning, part of speech tagging and parsing, neural sequence models, machine translation and dialogue systems)
- Fundamentals of computer text processing (file handling, tokenisation and normalisation, regular expressions) and powerful NLP/machine learning packages
Intellectual skills
Students who successfully complete this course will develop and demonstrate skills in:
- Adapting theories and intuitions to messy real-world data
- Scaling up theories and intuitions to big data
- Thinking formally about uncertainty and ambiguity
- Developing and analysing formal algorithms and procedure
Practical skills
Students who successfully complete this course will develop and demonstrate the ability to:
- Perform the simple text processing tasks (input/output, tokenisation, normalisation) needed to make use of NLP and machine learning packages
- Run computational linguistic experiments in provided Python notebooks
- Interpret and report on the performance of natural language processing systems
- Decide which algorithms to deploy for a new NLP problem
Transferable skills and personal qualities
Students who successfully complete this course will develop and demonstrate the ability to:
- Organise and access data in the cloud
- Run programs in the cloud - Apply training to an unfamiliar domain
- Deal with points of incompatibility between their prior assumptions and data
Assessment methods
Exam | 50% |
Coursework | 50% |
Mock exam | N/A (Formative) |
Feedback methods
Written and oral feedback on coursework report | Summative |
Written and oral feedback on exam | Summative |
Oral feedback on mock exam | Formative |
Recommended reading
Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. O'Reilly. http://www.nltk.org/book
Jurafsky, D. and J. H. Martin (2020), Speech and language processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. 3rd Edition. Prentice-Hall. https://web.stanford.edu/~jurafsky/slp3
Young, S. (2021). Hey Cyba: The Inner Workings of a Virtual Personal Assistant. Cambridge University Press.
Study hours
Scheduled activity hours | |
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Lectures | 11 |
Seminars | 22 |
Independent study hours | |
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Independent study | 167 |
Teaching staff
Staff member | Role |
---|---|
Colin James Bannard | Unit coordinator |