Linguistic Data: Quantitative Analysis and Visualisation: computational linguistics

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  • Instructors: Ilya Schurov and Olga Lyashevskaya

Materials

Data Topics Links
Jan 18 Introduction. Quantitative linguistic research and data types. R basics Intro Slides Lab 01: intro to R
Jan 25 Hypothesis testing. Binomial test. R: dataframes, tydyverse Lab 02 tidyverse cheat sheet
Feb 1 Central limit theorem. Variance. Student's t-test. R: simulating data, boxplots, density plots, binomial test, t-test

Lab 03: Rmd html Viz. distributions

Feb 8 Two-sample t-test. Paired t-test. Confidence intervals. Lab 04: Rmd pdfCI slides CI demo
Feb 15 ANOVA. Correlations Lab 05: Rmd pdf
Feb 22 Tests for categorial data. Chi-squared test. Fisher exact test. Effect size Lab 06: Rmd pdf DataCamp: contingency tables
Feb 29 Linear regression. Multivariate linear regression. Dummy variables

Lab 07: Rmd pdf html Common statistical tests & linear models

Mar 7 Fixed and random effects. Linear mixed-effects models

Lab 08. Part 1 Lab 08. Part 2. Rmd template LME models cheat sheet

Mar 21 Logistic regression. Model selection Lab 09
Mar 28 Dimensionality reduction. PCA. MDS. t-SNE
CA, MCA. Clusterization
Bootstrap. Decision trees. Decision forests
Bayesian statistics
Bayesian statistics II

Software

During this course we will use R as a programming language and RStudio as a GUI.

How to install R and RStudio?

1. Download R (you can choose another mirror here if you wish) and install it on your computer. Make sure you did it before installing RStudio.

2. Download RStudio (you need RStudio Desktop Open Source License) and install it on your computer. It is recommended to create a shortcut for RStudio during installation.

It is possible avoid installing anything on your PC, using rstudio.cloud (an online version of RStudio).

For successful submission of assignments you should be able to create and save R code files (.R) and RMarkdown files (.Rmd).


Homeworks

  • Homework 1 (deadline: February 16, 23:59), Chapters 1, 2, 3, and 5 of the DataCamp course "Introduction to R". Please fill in this form.
  • Homework 2 (deadline: February 23, 23:59), Chapters 4 and 6 of the DataCamp course "Introduction to R".
  • Homework 3 (deadline: February 9, 12:00), Hypothesis testing, binomial test, t-test. HW3 pdf html Rmd template
  • Homework 4 (deadline: February 29, 12:00), T-test and ANOVA, reproducing some results from Leivada & Westergaard 2019 HW4 pdf html Rmd template link to submit your .Rmd file
  • Homework 5 (deadline: March 09, 23:59), Contingency tables and tests, linear models HW5 pdf html Rmd template link to submit your .Rmd file
  • Homework 6 (due: March 28, 12:10), Mixed-effect models HW6 pdf html Rmd template [ link] to submit your .Rmd file]

Final project

  • Projects description link
  • Projects pre-registration: link to submit your file TBA
  • Final versions of project papers: link to sumbit your files TBA


References

  • Gries, Stefan (2013). Statistics for Linguistics with R : A Practical Introduction (Vol. 2nd revised edition). Berlin: De Gruyter Mouton. HSE library link
  • Levshina, Natalia (2015). How to Do Linguistics with R : Data Exploration and Statistical Analysis. Amsterdam: John Benjamins Publishing Company. HSE library link
  • Baayen, Harald (2008). Analyzing Linguistic Data: A practical introduction to statistics. Cambridge UP. pdf
  • Gries, Stefan (2017). Quantitative Corpus Linguistics with R : A Practical Introduction (Vol. Second edition). Milton Park, Abingdon, Oxon: Routledge. eBook
  • Empirical Bayes
  • Harney, H. L. (2016). Bayesian Inference : Data Evaluation and Decisions (Vol. 2nd ed). Springer. eBook
  • McElreath, R. (2016). Statistical Rethinking : A Bayesian Course with Examples in R and Stan. eBook
  • ggplot2
  • Hadley, W. (2016). Ggplot2 : Elegant Graphics for Data Analysis. Springer. eBook
  • R markdown [https://rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf Rmd Cheat Sheet

Course Info

This page contains the materials of the course "Linguistic Data: Quantitative Analysis and Visualisation", taught at the HSE Master's program "Computational Linguistics" in 2019-2020 academic year. Modules: 3-4.