Computational Modelling (Advanced)

Course description

Audience: Students who would like to improve their employability by learning a highly desirable skill. Students who would like to do any English Linguistic courses with a quantitative component in the future. It can also be beneficial to those who are more literature-based but would like to do more digital humanities. Students who are interested in Artificial Intelligence.

Description: Natural Language Processing plays a big role in our digital lives. We will demystify some of these everyday tasks that involve natural language processing: such as spelling and grammar correction, document classification, dialogue systems, machine translation, and forensic linguistics. On the practical side, we will focus on applying off-the-shelf tools that are often used in computational modelling of language data. Armed with these skills, you will be able to model language data quantitatively and ask measurable research questions By the end of the course, you will learn how to perform i) pre-processing of text files (cleaning up raw text files), ii) automatic linguistic annotation, such as Part of Speech tagging (automatically adding labels such as Noun, Adjective to each word), Name Entity Recognition (identifying proper names, time, date, places, events) and Sentiment (fear, anger, happy, surprise…) iii) the basics of classifying documents, authors and sentiment. Students will get insight into how these systems work (and why it is still so difficult to do natural language processing well). We also consider social and ethical considerations such as privacy, job creation and loss due to language technologies, and the nature of consciousness and machine intelligence.

Textbook: Dickinson, M., Brew, C., & Meurers, D. (2012). Language and computers. John Wiley &