This page consists of the links to lecture slides and assignments. Lecture videos are shared in a private folder to registered participants. For details on the deadlines for assignments/termpaper check your emails.

Topic 0: Introduction

  1. Course Overview: slides
  2. NLP Overview: slides
  3. NLP in Economics - an Overview: slides

Assignment 0 - Description

Meeting on: 16th October 2020

Online discussion notes/summary

Topic 1: Python overview

  1. Overview
  2. Python basics - overview - covers variables, conditionals, loops, functions, error handling, files etc
  3. Python data structures - overview - covers strings, lists, dictionaries, tuples etc
  4. An example code covering all python basics

Assignment 1 - Description

Meeting on: 16th October 2020

Online discussion notes/summary

Topic 2: Python and Text

  1. Overview
  2. Working with different file formats
  3. Text preprocessing
  4. Text representation

Assignment 2 - Description

Meeting on: 19th October 2020

Online discussion notes/summary

Topic 3: NLP and Machine Learning Methods: An Introduction

A note on installing required python libraries

  1. Overview
  2. Regular Expressions
  3. Corpus Collection
  4. Corpus Analysis
  5. Text Classification
    (code example)
  6. Information Extraction
  7. Topic Modeling
  8. Text Summarization

Assignment 3 - Description

Meeting on: 21st and 23rd October 2020

Online discussion notes/summary

Topic 4: NLP and Economics, Group Discussions


  1. Students are grouped into teams of 2-4 people and can choose a paper related to economics research that uses NLP methods. You can choose one of the papers from the readings list in course materials or pick one on your own.

  2. Teams should present for 10 min, we can have a 5-10 min additional discussion per team. Slides are optional.

  3. What to cover:

    • What problem relevant to economics is covered in the paper
    • What NLP methods are used?
    • How do they fare? What is your opinion about the paper?
    • What are some good ideas for future if you get a chance to work on this topic?

Meeting on: 26th and 28th October 2020 (Schedule with groups and papers will be put up towards the meeting dates!)

Team Members (first names) Paper Date/order
Pietro, Katharina, Lukas M. Fesler, L., Dee, T., Baker, R. & Evans, B. (2019). Text as data methods for education research. Journal of Research on Educational Effectiveness, 12(4), 707-727. 26/10, Group 1
Dominik, Julian, Clarissa Fetzer, T. (2020): Can Workfare Programs Moderate Conflict? Evidence from India. Journal of the European Economic Association. 26/10, Group 2
Tim, Felix,Emilio Gentzkow, M., Shapiro, J.M. and Taddy, M., 2019. Measuring group differences in high‐dimensional choices: method and application to congressional speech. Econometrica, 87(4), pp.1307-1340. 26/10, Group 3
Lena, Svenja, Silvia, Hoa Kolev, Julian and Fuentes-Medel, Yuly and Murray, Fiona E., Is Blinded Review Enough? How Gendered Outcomes Arise Even Under Anonymous Evaluation (April 2019). NBER Working Paper No. w25759. 28/10, Group 1
Leonie, Philipp, Raphaela Ros, R., van Erp, M., Rijpma, A., & Zijdeman, R. (2020). Mining Wages in Nineteenth-Century Job Advertisements. The Application of Language Resources and Language Technology to study Economic and Social Inequality. In Proceedings of the Workshop about Language Resources for the SSH Cloud (pp. 27-32). 28/10, Group 2
Jakob, Moritz, Lukas R Jelveh, Z., Kogut, B., & Naidu, S. (2014, October). Detecting latent ideology in expert text: Evidence from academic papers in economics. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1804-1809). 28/10, Group 3

Review session

Meeting on 30th October 2020

Term papers

Write a problem statement for a research question in your discipline that you think can benefit from using NLP methods. Give some background about the problem, existing ways of addressing it, and how NLP can help. Write about what NLP approaches you learnt in this class can be useful for this problem. You are free to read any related literature, but I want you to come up with a problem statement of your own. Your writeups can be 2-4 pages long, and contain all the above mentioned information.

There is no meeting afterwards. I will send you feedback by email.