MA Computational Linguistics

Module: 7LN004

Credits: 20

Period: 1

Type: Core

Locations: Wolverhampton City Campus

The aim of the module is to introduce you to the foundations of Computational Linguistics. You will acquire knowledge and skills required both for carrying out practical Natural Language Processing tasks and for theoretical studies of computational models of several linguistic phenomena.

Module: 7LN002

Credits: 20

Period: 1

Type: Core

Locations: Wolverhampton City Campus

A corpus is a large body of text stored on the computer, sampled for a specific purpose or linguistic analysis. The aim of the module is to introduce you to the foundations of Corpus Linguistics. You will acquire knowledge and skills required both for carrying out statistical analyses of corpora, and learn how corpora are used in specific applications, including machine translation, the study of the human translation process, and in finding the characteristics of learner language. R is a computer programming language for statistical calculations.

Module: 7LN007

Credits: 60

Period: 1

Type: Core

Locations: Wolverhampton City Campus

Note the dissertation will be worth 60 credits. In this module you will create and reflect on an artefact which provides a solution to a problem in the domain of computational linguistics. This module aims to develop your deep knowledge and understanding of a relevant NLP / computational linguistics topic. You will carry out in-depth research, analyse information and ideas and create informed responses to complex problems defined within the project area, learn how to critically evaluate sources and solutions and acquire a professional attitude to undertaking a NLP / computational linguistics project.

Module: 7LN008

Credits: 20

Period: 1

Type: Core

Locations: Wolverhampton City Campus

The module will enable you to acquire concepts about machine learning and understand how they can use machine learning for NLP applications. In practical terms, it will introduce you to various types of machine learning approaches and show how they can be employed for specific NLP applications. In critical terms, the module requires you to develop and evaluate the use of machine learning for NLP applications. Topics covered include how to determine the appropriate machine learning method for a specific application, supervised and unsupervised techniques and evaluation issues. You are expected to have intermediate knowledge of Python. Laboratory sessions will give students hands-on experience in using machine learning.

Module: 7LN001

Credits: 20

Period: 1

Type: Core

Locations: Wolverhampton City Campus

The module will enable you to acquire basic and intermediate concepts of computer science and programming in Python. The module is intended for linguists and other non-computer-scientists who have no programming experience, but computer scientists interested to learning Python and how it can be used in corpus linguistics can also benefit from it. Its special focus will be on Python and how it can be used to solve problems from corpus linguistics. Topics to be covered include: how to analyse the problem to be solved, fundamental data types, control structures, functions, regular expressions, simple tokenization, arrays, dictionaries, files, and corpora. Laboratory sessions will give participants hands-on experience in writing Python programs individually and in teams. The module will also introduce you to NLTK, a powerful package for language processing.

Module: 7LN006

Credits: 20

Period: 1

Type: Core

Locations: Wolverhampton City Campus

Module: 7LN003

Credits: 20

Period: 1

Type: Optional

Locations: Wolverhampton City Campus

Module: 7LN005

Credits: 20

Period: 1

Type: Optional

Locations: Wolverhampton City Campus

The aim of this module is to introduce you to the theoretical and practical aspects of translation technology (TT). You will acquire the knowledge and skills of electronic tools& nbsp;used by professional translators, such as translation memory systems (TMS), on-line resources and corpus management. Formative assessments including plans for essays and portfolios will allow you to receive feedback on your work at different points during the semester before the final summative assessments are due.

Figures speak louder than words: the University of Wolverhampton boasts an outstanding graduate employability rate – 96% of students are in work or further training six months after graduation!

Facilities

The course will be run on the City Campus, which is situated in the heart of the city centre, only a seven-minute walk from both the train station and St Georges Metro terminus, and a five-minute walk from the main bus station.

The newly renovated City Campus features:
- The Harrison Learning Centre, which has four floors of electronic, online, hardcopy and audio-visual materials;
- The Technology Centre, which has 500 PCs available for your personal use;
- A 'Social Learning Space', which incorporates a coffee and sandwich bar with islands of PCs and comfortable seating;
- On-campus food court, shops, and outlets such as Starbucks;
- Sports facilities including a gym and a sports hall;
- Three Halls of Residence for 1,000 students, located only a short walk from the campus and next to a 24-hour supermarket;
- City centre location, close to all amenities (post office, restaurants, shopping centres, art gallery, theatre etc.);
- Excellent train connections to all major cities (Birmingham: 20 minutes, London: 1 hour 50 minutes).

The researchers leading the course are international experts in their fields.

Dr. Michael Oakes
Course Leader and Reader in Computational Linguistics
Research Group in Computational Linguistics
Dr. Oakes is the author of the books “Statistics for Corpus Linguistics” and “Literary Detective Work on the Computer”.
Modules: Computational Linguistics, Corpus Linguistics with R.

Dr. Frédéric Blain
Lecturer of Translation Technology
Research Group in Computational Linguistics
Dr. Blain is co-organiser of the international shared task on Quality Estimation at the Conference on Machine Translation (WMT
Modules: Machine Translation (lead), NLP seminars (lead), EMTTI Specialised Seminars (co-lead)

Dr. Burcu Can
Reader in Computational Linguistics
Module Leader of Machine Learning for NLP
Research Group in Computational Linguistics
Modules: Machine Learning for NLP

Dr. Raheem Sarwar
Lecturer in Natural Language Processing
Research Group in Computational Linguistics
Dr. Sarwar has more than 5 years of hands-on experience in NLP, AI, Software Development and has co-authored over 20 peer-reviewed scholarly articles
Modules:Python Programming

Dr. Victoria Yaneva
Research Associate
Research Group in Computational Linguistics
Dr. Yaneva was recently featured on ITV news for her work on simplifying text for people with autism.
Modules:Research Methods and Professional Skills

Guest lectures will be given by Prof. Patrick Hanks, a world authority in Lexicography, and Prof. Ruslan Mitkov, Director of the Research Institute of Information and Language Processing, Editor of the “Oxford Handbook of Natural Language Processing” and Executive Editor of the Cambridge Journal “Natural Language Engineering”.

Learn more about our Research Group through visiting our website. Find out about current members, recent news, projects and read past papers written.

Follow us on Twitter to keep up to date with our latest news and developments at @RGCL_WLV.

Watch recently graduated PhD student and now Research Associate, Dr. Victoria Yaneva, share her research on ITV Central into innovative technology to assist people with Autistic Spectrum Disorder with their digital text comprehension.

Find out about Dr. Vinita Nahar’s (past group member) innovative research into technology to detect Cyberbullying online

The practical sessions include working with tools and software and developing programs based on the material taught in the lectures, allowing you to apply the technical skills you are learning. Some of the tasks are group based, feeding into the collaboration aspect of blended learning which enhances team-working skills, and some are done individually. Through portfolio building, you will be able to share your learning with other students. You will also be able to enhance your employability by sharing your online portfolio with prospective employers. Some assessments will require you to present your work to the rest of the class, enabling you to develop your presentation skills, which are useful in both academia and industry. Other transferrable skills are the abilities to structure your thoughts, present your ideas clearly in writing and prepare texts for a wider audience. You will acquire these skills through assessed report and essay writing, and most of all through writing your dissertation.

Location Mode Fee Year Home/EU Full-time £6400 per year 2020-21