Abstract
The field of Natural Language Processing (NLP) changes rapidly, requiring course offerings to adjust with those changes, and NLP is not just for computer scientists; it’s a field that should be accessible to anyone who has a sufficient background. In this paper, I explain how students with Computer Science and Data Science backgrounds can be well-prepared for an upper-division NLP course at a large state university. The course covers probability and information theory, elementary linguistics, machine and deep learning, with an attempt to balance theoretical ideas and concepts with practical applications. I explain the course objectives, topics and assignments, reflect on adjustments to the course over the last four years, as well as feedback from students.- Anthology ID:
- 2021.teachingnlp-1.21
- Volume:
- Proceedings of the Fifth Workshop on Teaching NLP
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- David Jurgens, Varada Kolhatkar, Lucy Li, Margot Mieskes, Ted Pedersen
- Venue:
- TeachingNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 115–124
- Language:
- URL:
- https://aclanthology.org/2021.teachingnlp-1.21
- DOI:
- 10.18653/v1/2021.teachingnlp-1.21
- Cite (ACL):
- Casey Kennington. 2021. Natural Language Processing for Computer Scientists and Data Scientists at a Large State University. In Proceedings of the Fifth Workshop on Teaching NLP, pages 115–124, Online. Association for Computational Linguistics.
- Cite (Informal):
- Natural Language Processing for Computer Scientists and Data Scientists at a Large State University (Kennington, TeachingNLP 2021)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2021.teachingnlp-1.21.pdf