Cited by 263Topic ModelingNatural Language Processing TechniquesSentiment Analysis and Opinion Mining

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TimeLMs: Diachronic Language Models from Twitter

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Loureiro, Daniel, Barbieri, Francesco, Neves, Leonardo, Espinosa Anke, Luis, Camacho-collados, Jose·2022·Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

DOI 10.18653/v1/2022.acl-demo.25

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Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, Jose Camacho-collados. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2022.

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Authors: Loureiro, D., Barbieri, F., Neves, L., Espinosa Anke, L., & Camacho-collados, J.
Affiliations: Universidade do Porto (PT)
ORCID: Loureiro, Daniel
Barbieri, Francesco
Venue: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Publisher: Association for Computational Linguistics
Published: 2022
Pages: 251-260
DOI: 10.18653/v1/2022.acl-demo.25
OpenAlex: W4285190530
Cited by: 263
References: 36
Citations by year: 2026: 55 · 2025: 66 · 2024: 68 · 2023: 51 · 2022: 23
FWCI: 28.76
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) (publishedVersion, cc-by)
ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords: Twitter, social media text, language change

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TimeLMs: Diachronic Language Models from Twitter

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