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Large language models (LLMs) have been leveraged for several years now, obtaining state-of-the-art performance in recognizing entities from modern documents.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
Intentional Systems Theory
Daniel Dennett. 2009 · 2009
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In 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) . Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Towards Robust Named Entity Recognition for Historic German. In Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019) . Association for Computational Linguistics, Florence, Italy, 96–103
Stefan Schweter and Johannes Baiter. 2019 · 2019
Earlier work this paper cites.
LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation. In Proceedings of the Twelfth Language Resources and Evaluation Conference . European Language Resources Association, Marseille, France, 1803–1813
Gustavo Aguilar, Sudipta Kar, and Thamar Solorio. 2020 · 2020
Earlier work this paper cites.
Alleviating digitization errors in named entity recognition for historical documents. In Proceedings of the 24th conference on computational natural language learning . 431–441
Emanuela Boroş, Ahmed Hamdi, Elvys Linhares Pontes, Luis-Adrián Cabrera-Diego, Jose G Moreno, Nicolas Sidere, and Antoine Doucet. 2020 · 2020
Earlier work this paper cites.
Robust named entity recognition and linking on historical multilingual documents. In Conference and Labs of the Evaluation Forum (CLEF 2020) , Vol. 2696. CEUR-WS Working Notes, 1–17
Emanuela Boros, Elvys Linhares Pontes, Luis Adrián Cabrera-Diego, Ahmed Hamdi, José G Moreno, Nicolas Sidère, and Antoine Doucet. 2020 · 2020
Earlier work this paper cites.
Language resources for historical newspapers: the impresso collection
Maud Ehrmann, Matteo Romanello, Simon Clematide, Phillip Ströbel, and Raphaël Barman. 2020a · 2020
Earlier work this paper cites.
Extended overview of CLEF HIPE 2020: named entity processing on historical newspapers. In CEUR Workshop Proceedings . CEUR-WS
Maud Ehrmann, Matteo Romanello, Alex Flückiger, and Simon Clematide. 2020b · 2020
Earlier work this paper cites.
GPT-3: Its nature, scope, limits, and consequences
Luciano Floridi and Massimo Chiriatti. 2020 · 2020
Earlier work this paper cites.
A survey on deep learning for named entity recognition
Jing Li, Aixin Sun, Jianglei Han, and Chenliang Li. 2020 · 2020
Cited alongside, same era.
How Linguistically Fair Are Multilingual Pre-Trained Language Models?. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 12710–12718
Monojit Choudhury and Amit Deshpande. 2021 · 2021
Cited alongside, same era.
Named entity recognition and classification on historical documents: A survey
Maud Ehrmann, Ahmed Hamdi, Elvys Linhares Pontes, Matteo Romanello, and Antoine Doucet. 2021 · 2021
Cited alongside, same era.
True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
Cited alongside, same era.
Optical Character Recognition of 19th Century Classical Commentaries: The Current State of Affairs. In The 6th International Workshop on Historical Document Imaging and Processing (Lausanne, Switzerland) (HIP ’21) . Association for Computing Machinery, New York, NY, USA, 1–6
Page Layout Analysis of Text-heavy Historical Documents: a Comparison of Textual and Visual Approaches. In Proceedings of the Computational Humanities Research Conference 2022 Antwerp, Belgium, December 12-14, 2022. 36–54
Sven Najem-Meyer and Matteo Romanello. 2022 · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Later among the works it cites.
Guidelines for the Annotation of Named Entities in the Domain of Classics
Matteo Romanello and Sven Najem-Meyer. 2022 · 2022
Later among the works it cites.
hmbert: Historical multilingual language models for named entity recognition
Stefan Schweter, Luisa März, Katharina Schmid, and Erion Çano. 2022 · 2022
Later among the works it cites.
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Matteo Romanello, Sven Najem-Meyer, and Bruce Robertson. 2021 · 2021
Cited alongside, same era.
Are Multilingual Models Effective in Code-Switching?. In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching . Association for Computational Linguistics, Online, 142–153
Genta Indra Winata, Samuel Cahyawijaya, Zihan Liu, Zhaojiang Lin, Andrea Madotto, and Pascale Fung. 2021 · 2021
Cited alongside, same era.
BERToldo, the Historical BERT for Italian. In Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages . 68–72
Alessio Palmero Aprosio, Stefano Menini, and Sara Tonelli. 2022 · 2022
Cited alongside, same era.
Knowledge-based Contexts for Historical Named Entity Recognition & Linking
Emanuela Boros, Carlos-Emiliano González-Gallardo, Edward Giamphy, Ahmed Hamdi, José G Moreno, and Antoine Doucet. 2022 · 2022
Cited alongside, same era.
Extended Overview of HIPE-2022: Named Entity Recognition and Linking in Multilingual Historical Documents. In Proceedings of the 13th International Conference of the CLEF Association (Lecture Notes in Computer Science) , Vol. 13390. Springer
Maud Ehrmann, Matteo Romanello, Sven Najem-Meyer, Antoine Doucet, and Simon Clematide. 2022 · 2022
Cited alongside, same era.
Adapting vs. Pre-training Language Models for Historical Languages
Lauren Fonteyn and Enrique Manjavacas. 2022 · 2022
Cited alongside, same era.
Impresso Named Entity Annotation Guidelines
Ehrmann, Watter, Romanello, Clematide, and Flückiger. 2020c
Cited in the paper.
A multilingual dataset for named entity recognition, entity linking and stance detection in historical newspapers. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2328–2334
Ahmed Hamdi, Elvys Linhares Pontes, Emanuela Boros, Thi Tuyet Hai Nguyen, Günter Hackl, Jose G Moreno, and Antoine Doucet. 2021a
Cited in the paper.
Zeerak Talat, Aurélie Névéol, Stella Biderman, Miruna Clinciu, Manan Dey, Shayne Longpre, Sasha Luccioni, Maraim Masoud, Margaret Mitchell, Dragomir Radev, et al · 2022
Later among the works it cites.
ChatGPT and the Future of Medical Writing
Som Biswas. 2023 · 2023
Closest in time.
Injecting Temporal-aware Knowledge in Historical Named Entity Recognition. In Advances in Information Retrieval: 45th European Conference on IR Research, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part I . Springer, 65–79
Carlos-Emiliano González-Gallardo, Emanuela Boros, Edward Giamphy, Ahmed Hamdi, José G Moreno, and Antoine Doucet. 2023 · 2023
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Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education
John V Pavlik. 2023 · 2023
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Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023 · 2023
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