Linguistic Data: Quantitative Analysis and Visualisation

Master 2020/2021

Category 'Best Course for Career Development'

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'

Category 'Best Course for New Knowledge and Skills'

Type: Compulsory course (Linguistic Theory and Language Description)

Area of studies: Fundamental and Applied Linguistics

Delivered by: School of Linguistics

Where: Faculty of Humanities

When: 2 year, 1, 2 module

Mode of studies: offline

Instructors: Daria Popova

Master’s programme: Linguistic Theory and Language Description

Language: English

ECTS credits: 3

Contact hours: 32

Full Syllabus Ask Question

Abstract

Preprocessing of linguistic data in Python is designed to further the students’ knowledge of natural language processing and to polish their programming skills. The course aims to provide the students with the programming and natural language processing knowledge and competencies necessary to plan and conduct research projects of their own leading to the M.Sc. dissertation and scientific publications.

Learning Objectives

to further the students’ programming skills
to provide the students with the necessary skills to write programs for experiments and corpus studies
to teach the students how to re-format data
to teach the students how to retrieve data from the Internet
to teach the students how to write their code so that it is readable by other linguists
to teach the students how to present their research that involves coding in the written and in the oral form
to provide an overview of some of the most exciting current computational projects
to teach the students how to read and to assess critically linguistic research that uses computational methods
to teach the students how to formulate linguistic questions in a way that can be addressed computationally
to teach the students to conduct independent computational studies

Expected Learning Outcomes

is able to re-format data
writes programs (code) for experiments and corpus studies
writes their code so that it is readable by other linguists and programmers
retrieves data from the Internet
presents their research that involves coding in the written and in the oral form
reads and assesses critically linguistic research that uses computational methods
formulates linguistic questions in a way that can be addressed computationally
conducts independent natural language processing studies

Course Contents

Datatypes and variables
Variables assignment, basic datatypes, mutability.
Control structures
Grouping and indentation. If, for, while, break and continue.
Input and output
Command-line input, keyboard input, file input and output.
Subroutines and modules
Simple functions, functions that return values, functions that take arguments, recursive functions, modules, writing modules. Classes.
Regular expressions
Matching, searching for patterns, patterns.
Text manipulation
Tokenization, stemming, parsing different data formats.
Internet data
Retrieving webpages, HTML, parsing HTML, webcrawlers.
Different data formats: csv, databases, json
Basics of web design: creating a web site for a linguistic experiment
Word2vec
Graphs in Python

Assessment Elements

homework assignment 1
homework assignment 2
in-class presentation
экзамен (final project)

Interim Assessment

Interim assessment (2 module)
0.25 * homework assignment 1 + 0.25 * homework assignment 2 + 0.2 * in-class presentation + 0.3 * экзамен (final project)

Bibliography

Recommended Core Bibliography

Perkins, J. (2014). Python 3 Text Processing with NLTK 3 Cookbook. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=836632

Recommended Additional Bibliography

Romano, F. (2015). Learning Python. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1133614

Course Syllabus