Adapting natural language processing for technical text-Reference-Cited by-同舟云学术

Adapting natural language processing for technical text

Published:2021-06-30 Issue:3 Volume:2 Page:
ISSN:2689-5595
Container-title:Applied AI Letters
language:en
Short-container-title:Applied AI Letters

Author:

Dima Alden¹^ORCID,Lukens Sarah²,Hodkiewicz Melinda³^ORCID,Sexton Thurston⁴^ORCID,Brundage Michael P.⁴^ORCID

Affiliation:

1. Information Technology Laboratory National Institute of Standards and Technology Gaithersburg Maryland USA

2. GE Digital San Ramon California USA

3. Faculty of Engineering and Mathematical Sciences The University of Western Australia Perth Western Australia Australia

4. Engineering Laboratory National Institute of Standards and Technology Gaithersburg Maryland USA

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ail2.33

Reference54 articles.

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2. EmersonG.What are the goals of distributional semantics? Paper presented at: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics;2020:7436‐7453.

3. LinzenT.How can we accelerate progress towards human‐like linguistic generalization? Paper presented at: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics;2020:5210‐5217.

4. HeavenWD.OpenAI's new language generator GPT‐3 is shockingly good—and completely mindless;2020.https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-gpt-3-nlp/. Accessed December 8 2020.

5. BrownTB MannB RyderN et al.Language models are few‐shot learners.arXiv:2005.14165;2020.

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