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Information Extraction (IE) seeks to derive structured information from unstructured texts, often facing challenges in low-resource scenarios due to data scarcity and unseen classes.
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W. Liu, S. Cheng, D. Zeng, and H. Qu, “Enhancing document-level event argument extraction with contextual clues and role relevance,” in ACL (Findings) . Association for Computational Linguistics, 2023, pp. 12 908–12 922. [Online]. Available: https://doi.org/10.18653/v1/2023.findings-acl.817
2023
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W. Zhou, S. Zhang, Y. Gu, M. Chen, and H. Poon, “Universalner: Targeted distillation from large language models for open named entity recognition,” in ICLR . OpenReview.net, 2024. [Online]. Available: https://openreview.net/forum?id=r65xfUb76p
2024
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J. Shang, L. Liu, X. Gu, X. Ren, T. Ren, and J. Han, “Learning named entity tagger using domain-specific dictionary,” in EMNLP . Association for Computational Linguistics, 2018, pp. 2054–2064. [Online]. Available: https://doi.org/10.18653/v1/d18-1230
2064
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B. Zhou, D. Khashabi, C. Tsai, and D. Roth, “Zero-shot open entity typing as type-compatible grounding,” in EMNLP . Association for Computational Linguistics, 2018, pp. 2065–2076. [Online]. Available: https://doi.org/10.18653/v1/d18-1231
2076
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X. Ma, X. Qin, J. Liu, and W. Ran, “Interaction information guided prototype representation rectification for few-shot relation extraction,” Electronics , vol. 12, no. 13, 2023. [Online]. Available: https://www.mdpi.com/2079-9292/12/13/2912
2079
Closest in time.