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 13.11.2020
Time and Place Friday, 10:30-12:00, Online
Commitment Period tbd.

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)
  • Assessment

    Content

    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

    Agenda

    Date Session Materials

    Literature

    • 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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