The DOI matches its Crossref record; 37 of the 38 references it cites were found in a registry or an index, and 1 are not checked yet.
TimeLMs: Diachronic Language Models from Twitter
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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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“We then classified the sentences into positive, neutral or negative using a sentiment classifier based on RoBERTa49, which was fine-tuned for sentiment classification50.”
From Generative language models exhibit social identity biases (2024) · cites this paper as [50]
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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz682 · Cited by 7,218
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5446 · Cited by 4,222
SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1371 · Cited by 3,218
Domain-specific language model pretraining for biomedical natural language processing
Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3458754 · Cited by 2,170
On the resemblance and containment of documents
Broder, A. (2002) · Compression and Complexity of Sequences (SEQUENCES) · doi:10.1109/sequen.1997.666900 · Cited by 1,818
Gradient Episodic Memory for Continual Learning
Lopez-Paz, D., & Ranzato, M. (2017) · arXiv · doi:10.48550/arxiv.1706.08840 · Cited by 1,403
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
Hamilton, W. L., Leskovec, J., & Jurafsky, D. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1141 · Cited by 1,170
Research works printed but not linked: 1
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
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ChatGPT: Jack of all trades, master of none
Kocoń, J., Cichecki, I., Kaszyca, O., Kochanek, M., Szydło, D., Baran, J., Bielaniewicz, J., Gruza, M., Janz, A., Kanclerz, K., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101861 · Cited by 758
A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM
Miah, M. S. U., Kabir, M. M., Sarwar, T. B., Safran, M., Alfarhood, S., & Mridha, M. F. (2024) · Scientific Reports · doi:10.1038/s41598-024-60210-7 · Cited by 210
TweetNLP: Cutting-Edge Natural Language Processing for Social Media
Camacho-collados, J., Rezaee, K., Riahi, T., Ushio, A., Loureiro, D., Antypas, D., Boisson, J., Espinosa Anke, L., Liu, F., & Martínez Cámara, E. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2022.emnlp-demos.5 · Cited by 142
Self-citationPellert, M., Lechner, C. M., Wagner, C., Rammstedt, B., & Strohmaier, M. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231214460 · Cited by 97
Generative language models exhibit social identity biases
Hu, T., Kyrychenko, Y., Rathje, S., Collier, N., van der Linden, S., & Roozenbeek, J. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00741-1 · Cited by 100
“We then classified the sentences into positive, neutral, or negative with a sentiment classification RoBERTa model [45] and assessed a more fine-grained sentence sentiment score using VADER [46].”
Metadata
- Authors
- Loureiro, D., Barbieri, F., Neves, L., Espinosa Anke, L., & Camacho-collados, J.
- Affiliations
- Universidade do Porto (PT)
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
- Published
- 2022
- Pages
- 251-260
- 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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