The knowledge and skills developed in the course will be assessed in a variety of ways. Assessments will include writing assignments on given topics, reports on practical work carried out in the class, portfolios, projects, oral presentations, and tests.
The culmination of the study programme will be your 15,000-word dissertation, which will allow you to carry out an in-depth study of a chosen topic within the areas of corpus linguistics, language teaching, lexicography, or translation.
7LN001 Python Programming
Dr. Raheem Sarwar
You will be taught the Python computer programming language, which is specially designed for dealing with natural language texts. 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.
7LN002 Corpus Linguistics with R
Dr. Michael Oakes
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 programming language to study the statistics of language.
7LN004 Computational Linguistics
Dr. Michael Oakes, Prof. Patrick Hanks, Dr. Sara Moze
This is the use of computers to study language at all levels, such as parts of words, individual words, relations between words, syntax (the arrangement of words in a sentence), semantics (the study of meaning) and discourse phenomena such as anaphora resolution (how can we tell when the same entity is referred to repeatedly in a text?). Other topics include tokenisation (splitting texts into individual words), part of speech tagging (assigning a grammatical part of speech to each word in an input text), information theory, measures of similarity and difference between texts, and computer lexicography (using computers to help make dictionaries).
7LN005 Translation Technology for Professional Translators
Dr. Emad Mohamed, Dr. Fred Blain
The aim of this module is to introduce you to the theoretical and practical aspects of translation technology (TT). You will acquire knowledge and skills of electronic tools used by professional translators, such as translation memories 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.
Translation Tools for Professional Translators is an elective module that may be chosen in the Second Semester to replace another taught module for those students who are interested in pursuing careers in Translation.
7LN006 Research Methods and Professional Skills
Dr. Emad Mohamed
The primary aim of this module is to develop your knowledge and experience of research methods and techniques in the field of computing and information systems. In particular, to develop professionalism in the acquisition and deployment of appropriate research skills in areas such as ethics, data collection, documentation, and presentation. You will learn how to design an experiment to thoroughly test your research questions.
7LN008 Machine Learning
Dr. Burcu Can
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.
Supervisors drawn from RIILP.
This module will allow you to produce a major piece of independent study, combining original research with thorough analysis of the established literature in the chosen area (i.e. corpus linguistics, lexicography, translation studies, or language teaching). You will work independently, under the guidance of a supervisor. In the dissertation, you will demonstrate comprehensive theoretical and practical knowledge of a specific linguistic topic, as well as effective use of subject-specific and transferable research skills, including corpus linguistic methods and academic writing. The ability to successfully complete the dissertation is a significant component in demonstrating Masters level study.