MSc Computational and Corpus Linguistics

This course unit is designed to equip students with foundational skills in reading, conducting and presenting computational and corpus linguistic research. It covers the relationship between empirical evidence and theoretical models, project design, and strategies of presenting and writing up linguistic research. It also provides training in reading and assessing cross-linguistic data and in the use of tools including the programming language Python. 

The unit aims to:

• Enable students to understand the relationship between empirical evidence and theoretical models
• Introduce and provide practice in the writing conventions and main academic genres in linguistics
• Develop student competence with core digital tools of Computational and Corpus Linguistics  
• Provide experience with the University of Manchester’s High Performance Computing Facilities 

Week 1. Intro to the scientific method

Week 2. Intro to Python for Corpus Processing

Week 3. Research Questions in Computational and Corpus Linguistics

Week 4. Writing about Research in Linguistics; using the library/referencing  

Week 5. Interpreting and Annotating Cross-Linguistic Data

Week 6. Python Programming for Computational Linguists 1

Week 7. Python Programming for Computational Linguists 2

Week 8. Introduction to Unix and cluster computing

Week 9.   Using version management and containerisation.

Week 10. Designing an MSc dissertation project

Week 11. Designing an MSc dissertation project 

Students will attend weekly synchronous 90 minutes seminars. Five of these sessions will involve computer-based activities which will be completed with Jupyter notebooks provided by the instructor.  These are documents that combine text and computer code that can be edited and run within the document. Students will access them via Blackboard and run them either on their local machine or using a free cloud computing service such as Google Colab. The sessions will consist of collectively working through the notebook, with students being able to run code provided, combined with exercises that students will complete individually or in small groups. The instructor will circulate and provide assistance as needed. On occasion the whole class will collaborate to provide a solution.

Other seminars will focus on the analysis and discussion of linguistic data, the collective criticism and revision of presented linguistic analyses or on problem-based activities where students are presented with a challenge to which they have collectively to find a problem. 

Students will be able to: 

• understand and engage in scholarly discussion about the relationship between theory and analysis in linguistics
• evaluate linguistic theories in relation to evidence  
• appropriate language-specific analyses in cases of cross-linguistic differences 

Students will be able to: 

• Identify and discuss research questions 
• recognize how theoretical assumptions shape linguistic analysis in academic articles
• assess the extent to which a piece of evidence supports or challenges a theoretical model in linguistics 

Students will be able to:

• use bibliographic databases to inform linguistic research 
• follow academic writing conventions in linguistics 
• write computer programs in Python for text analysis and machine learning 
• manage computational research projects using version control and containerisation 
• run programs on the UoM Computational Shared Facility 

Students will be able to: 

• summarize and present research findings in a concise and effective manner
• reflect critically on the empirical evidence for a claim or argument  
   

Oral feedback from academic and peers for the in class programming tasks

Written feedback for the Written Summary and Programming Exercise. 

Immediate feedback upon submission of the quizzes.