Ruprecht-Karls-Universität Heidelberg
Institut für Computerlinguistik

Bilder vom Neuenheimer Feld, Heidelberg und der Universität Heidelberg

NLP in Industry: challenges and best practices

Module Description

Course Module Abbreviation Credit Points
BA-2010[100%|75%] CS-CL 6 LP
BA-2010[50%] BS-CL 6 LP
BA-2010 AS-CL 8 LP
Master SS-CL, SS-TAC 8 LP
Lecturer Daniel Dahlmeier
Module Type Proseminar / Hauptseminar
Language English
First Session 24.04.2020
Time and Place
Friday, 14:15-15:45, INF 327 / SR 2 (or virtual if required)
daily, 09:15-16:45, INF 306 / SR 13 (or virtual if required)
Commitment Period tbd.


To sign up for the class, send an email to d.dahlmeier at The class consists of two parts; first weekly meetings on fridays during the summer term, then a week of block sessions mid August.

Prerequisite for Participation

-Mathematical Foundations of CL (or a comparable introductory class to linear algebra and theory of probability) -Programming I (Python)

-Statistical Methods for CL (or a comparable introductory class to machine learning)


  • Regular and active attendance of seminar (40%)
  • Project: novelty of idea, empirical results, clarity of report and presentation (60%)


This seminar focuses on common challenges and best practices for natural language processing (NLP) in industry. NLP in industry comes with its own challenges, like data sparsity, data privacy regulations, and cost-benefit trade-offs. In this seminar, we discuss these challenges and technical approaches to overcome them. The seminar includes a practical project where participants propose a project, implement experiments and present the results to the class.

Module Overview


Date Session Materials


  • Daniel Jurafsky, James H. Martin. 2008. Speech and Language Processing (2nd Edition)
  • Alfred Spector, Peter Norvig, and Slav Petrov. 2012.Google's hybrid approach to research. Communications of the ACM 55(7).
  • Daniel Dahlmeier. 2017. On the Challenges of Translating NLP Research into Commercial Products. In Proceedings of ACL
  • D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, and Michael Young. 2014. Machine learning: The high interest credit card of technical debt. In Proceedings of SE4ML: Software Engineering for Machine Learning (NIPS 2014 Workshop)

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