@inproceedings{kennington-2021-natural,
title = "Natural Language Processing for Computer Scientists and Data Scientists at a Large State University",
author = "Kennington, Casey",
editor = "Jurgens, David and
Kolhatkar, Varada and
Li, Lucy and
Mieskes, Margot and
Pedersen, Ted",
booktitle = "Proceedings of the Fifth Workshop on Teaching NLP",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.teachingnlp-1.21",
doi = "10.18653/v1/2021.teachingnlp-1.21",
pages = "115--124",
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.",
}
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%0 Conference Proceedings
%T Natural Language Processing for Computer Scientists and Data Scientists at a Large State University
%A Kennington, Casey
%Y Jurgens, David
%Y Kolhatkar, Varada
%Y Li, Lucy
%Y Mieskes, Margot
%Y Pedersen, Ted
%S Proceedings of the Fifth Workshop on Teaching NLP
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F kennington-2021-natural
%X 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.
%R 10.18653/v1/2021.teachingnlp-1.21
%U https://aclanthology.org/2021.teachingnlp-1.21
%U https://doi.org/10.18653/v1/2021.teachingnlp-1.21
%P 115-124
Markdown (Informal)
[Natural Language Processing for Computer Scientists and Data Scientists at a Large State University](https://aclanthology.org/2021.teachingnlp-1.21) (Kennington, TeachingNLP 2021)
ACL