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 |
21.04.2023 |
| Time and Place |
Friday, 08:15-09:45, online |
| Commitment Period |
tbd. |
Prerequisite for Participation
Mathematical Foundations of CL (or a comparable introductory class to linear algebra and theory of probability)
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. We will read and discuss recent industry-related publications, in particular from the NAACL 2022 industry track. Every student will get to present one paper in the seminar and write a report.
For questions about the seminar, please send an email to pa262
uni-heidelberg.de
Module Overview
| Datum | Sitzung | Materialien |
| 21.04.23 | - Seminar logistics
- State of AI
- Research and innovation
- NLP in Industry
- Papers, Presentations, and Report
| - Alfred Spector, Peter Norvig, and Slav Petrov. 2012.Google's hybrid approach to research. Communications of the ACM 55(7).
- Dahlmeier. 2017. On the Challenges of Translating NLP Research into Commercial Products. In Proceedings of ACL
|
| 28.04.23 | - Data
- Data protection
- Legal considerations
- Industry Use Cases
| |
| 05.05.23 | - AI transformation
- Text classification
- Named Entity Recognition
- Advcanced NLP topics
| |
| 12.05.23 | - The business case for NLP
- MLOps: developing AI/NLP products
| |
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)
