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Ever-larger language models with ever-increasing capabilities are by now well-established text processing tools.
A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams and David Zipser · 1989
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Text chunking using transformation-based learning
Lance Ramshaw and Mitch Marcus · 1995
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Message Understanding Conference- 6: A brief history
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Nymble: a high-performance learning name-finder
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Unsupervised models for named entity classification
Michael Collins and Yoram Singer · 1999
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Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder · 2003
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Introduction to the bio-entity recognition task at JNLPBA
Nigel Collier, Tomoko Ohta, Yoshimasa Tsuruoka, Yuka Tateisi, and Jin-Dong Kim · 2004
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Unsupervised named-entity extraction from the web: An experimental study
Oren Etzioni, Michael Cafarella, Doug Downey, Ana-Maria Popescu, Tal Shaked, Stephen Soderland, Daniel S. Weld, and Alexander Yates · 2005
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Towards robust linguistic analysis using OntoNotes
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Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts
Shaodian Zhang and Noémie Elhadad · 2013
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Ncbi disease corpus: A resource for disease name recognition and concept normalization
Rezarta Islamaj Doğan, Robert Leaman, and Zhiyong Lu · 2014
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Results of the WNUT2017 shared task on novel and emerging entity recognition
Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham · 2017
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The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization
Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi Papadopoulou, David Sánchez, and Montserrat Batet · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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CrossWeigh: Training named entity tagger from imperfect annotations
Zihan Wang, Jingbo Shang, Liyuan Liu, Lihao Lu, Jiacheng Liu, and Jiawei Han · 2019
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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 · 2020
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Hierarchical contextualized representation for named entity recognition
Ying Luo, Fengshun Xiao, and Zhao Hai · 2020
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Entity extraction without language-specific resources
Paul McNamee and James Mayfield · 2020
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LUKE: Deep contextualized entity representations with entity-aware self-attention
Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, and Yuji Matsumoto · 2020
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Few-NERD: A few-shot named entity recognition dataset
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BioMedLM
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Contradiction detection in financial reports
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Learning from noisy labels for entity-centric information extraction
Wenxuan Zhou and Muhao Chen · 2021
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LasUIE: Unifying information extraction with latent adaptive structure-aware generative language model
Hao Fei, Shengqiong Wu, Jingye Li, Bobo Li, Fei Li, Libo Qin, Meishan Zhang, Min Zhang, and Tat-Seng Chua · 2022
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An algorithm for routing vectors in sequences, 2022
Franz A. Heinsen · 2022
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Domain-adapted dependency parsing for cross-domain named entity recognition
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GPT-4 technical report, 2023
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A survey of large language models, 2023
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