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home / papers / 2023 / ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT

ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2302.10205

Summary

Building information extraction (IE) systems usually requires costly labeled data. This paper asks whether a large language model can perform IE with no task-specific training, using only prompts. The authors propose ChatIE, which recasts zero-shot IE as a multi-turn dialogue with ChatGPT in two stages. First, the model is asked which element types (relations, entity types or event types) from a given schema occur in a sentence. Second, it is queried separately for each detected type, using templates that can be chained when one element depends on another. The answers are requested as lists or tables and then assembled into structured output. The framework is applied to entity-relation triple extraction, NER and event extraction on six English and Chinese datasets. It is compared with single-turn prompting, zero-shot UIE, few-shot models and full-shot models. ChatIE clearly outperforms plain single-turn ChatGPT and, on some datasets, beats fully supervised systems. This makes it a practical option for low-resource IE, where no annotated data is available.

Key points

  • Datasets: DuIE2.0 and NYT11-HRL for RE, MSRA and conllpp for NER, and DuEE1.0 and ACE05 for EE. DuIE2.0, MSRA and DuEE1.0 are Chinese.
  • Gain over single-turn prompting: Across the six datasets, ChatIE improves on single-turn ChatGPT prompting by 16.65 points on average.
  • Comparison with zero-shot and few-shot baselines:
    • It beats the zero-shot UIE baseline everywhere.
    • It roughly matches the 20-shot model on MSRA.
    • It exceeds 100-shot models on NYT11-HRL, conllpp and ACE05.
  • Comparison with full-shot models: The results section reports that ChatIE surpasses full-shot models trained on labeled data for DuIE2.0 and DuEE1.0.
  • Metrics: Micro F1 throughout. RE is scored with Rel on NYT11-HRL and the stricter Rel+ on DuIE2.0. NER requires exact boundary and type matches. EE uses word-level F1 on DuEE1.0 and entity-level F1 on ACE05.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Topics: Named entity recognition

Cited by

131 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 127 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2302.10205 · arXiv 2302.10205

@misc{jiang2023chatie,
  title = {{ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT}},
  author = {Jiang;, Xiang Wei; Xingyu Cui; Ning Cheng; Xiaobin Wang; Xin Zhang; Shen Huang; Pengjun Xie; Jinan Xu; Yufeng Chen; Meishan Zhang; Yong and Han, Wenjuan},
  year = {2023},
  doi = {10.48550/arxiv.2302.10205},
  eprint = {2302.10205},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2302.10205}
}

References

84 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag. 2022. Large language models are zero-shot clinical information extractors. arXiv preprint arXiv:2205.12689. find
  2. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901. find
  3. Lingjiao Chen, Matei Zaharia, and James Zou. 2023. How is chatgpt’s behavior changing over time? arXiv preprint arXiv:2307.09009. — [arXiv:2307.09009](https://arxiv.org/abs/2307.09009) find
  4. Xinya Du and Claire Cardie. 2020. Event extraction by answering (almost) natural questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 671– 683. find our card
  5. Matthew R Gormley, Mo Yu, and Mark Dredze. 2015. Improved relation extraction with feature-rich compositional embedding models. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1774–1784. find
  6. Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu. 2023. How close is chatgpt to human experts? comparison corpus, evaluation, and detection. arXiv preprint arxiv:2301.07597. find our card
  7. Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018. FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4803–480… find
  8. Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S Weld. 2011. Knowledge-based weak supervision for information extraction of overlapping relations. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pages 5… find
  9. Katharina Jeblick, Balthasar Schachtner, Jakob Dexl, Andreas Mittermeier, Anna Theresa Stüber, Johanna Topalis, Tobias Weber, Philipp Wesp, Bastian Oliver Sabel, Jens Ricke, et al. 2023. Chatgpt makes medicine easy to swallow: An exploratory case study on simplified radiology reports. European Radio… find
  10. Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, and Zhaopeng Tu. 2023. Is chatgpt a good translator? a preliminary study. arXiv preprint arXiv:2301.08745. find our card
  11. Michael R King. 2022. The future of ai in medicine: a perspective from a chatbot. Annals of Biomedical Engineering, pages 1–5. find
  12. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199– 22213. find our card
  13. Gina-Anne Levow. 2006. The third international Chinese language processing bakeoff: Word segmentation and named entity recognition. In Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, pages 108–117, Sydney, Australia. Association for Computational Linguistics. find
  14. Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, and Yong Zhu. 2020a. Event extraction as multi-turn question answering. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 829–838. find
  15. Jingye Li, Hao Fei, Jiang Liu, Shengqiong Wu, Meishan Zhang, Chong Teng, Donghong Ji, and Fei Li. 2021. Unified named entity recognition as word-word relation classification. ArXiv, abs/2112.10070. — [arXiv:2112.10070](https://arxiv.org/abs/2112.10070) find
  16. Shuangjie Li, Wei He, Yabing Shi, Wenbin Jiang, Haijin Liang, Ye Jiang, Yang Zhang, Yajuan Lyu, and Yong Zhu. 2019a. Duie: A large-scale chinese dataset for information extraction. In CCF International Conference on Natural Language Processing and Chinese Computing, pages 791–800. Springer. find
  17. Xiaoya Li, Xiaofei Sun, Yuxian Meng, Junjun Liang, Fei Wu, and Jiwei Li. 2020b. Dice loss for data-imbalanced NLP tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 465–476, Online. Association for Computational Linguistics. find
  18. Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, and Jiwei Li. 2019b. Entity-relation extraction as multi-turn question answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1340–1350. find
  19. Xinyu Li, Fayuan Li, Lu Pan, Yuguang Chen, Weihua Peng, Quan Wang, Yajuan Lyu, and Yong Zhu. 2020c. Duee: a large-scale dataset for chinese event extraction in real-world scenarios. In CCF International Conference on Natural Language Processing and Chinese Computing, pages 534–545. Springer. find
  20. Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, and Shaoyi Chen. 2021. Text2event: Controllable sequence-to-structure generation for end-to-end event extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the… find our card
  21. Yaojie Lu, Qing Liu, Dai Dai, Xinyan Xiao, Hongyu Lin, Xianpei Han, Le Sun, and Hua Wu. 2022. Unified structure generation for universal information extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5755–5772, Dublin… find
  22. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744. find
  23. Lev Ratinov and Dan Roth. 2009. Design challenges and misconceptions in named entity recognition. In Proceedings of the thirteenth conference on computational natural language learning (CoNLL-2009), pages 147–155. find our card
  24. Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010. Modeling relations and their mentions without labeled text. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 148–163. Springer. find
  25. Oscar Sainz, Itziar Gonzalez-Dios, Oier Lopez de Lacalle, Bonan Min, and Eneko Agirre. 2022a. Textual entailment for event argument extraction: Zero-and few-shot with multi-source learning. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2439–2455, Seattle, United Sta… find
  26. Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, Ander Barrena, and Eneko Agirre. 2021. Label verbalization and entailment for effective zero and few-shot relation extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1199–1212, Online and Punt… find
  27. Oscar Sainz, Haoling Qiu, Oier Lopez de Lacalle, Eneko Agirre, and Bonan Min. 2022b. ZS4IE: A toolkit for zero-shot information extraction with simple verbalizations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language… find
  28. Sunita Sarawagi et al. 2008. Information extraction. Foundations and Trends® in Databases, 1(3):261– 377. find
  29. Teo Susnjak. 2022. Chatgpt: The end of online exam integrity? arXiv preprint arXiv:2212.09292. — [arXiv:2212.09292](https://arxiv.org/abs/2212.09292) find
  30. Ryuichi Takanobu, Tianyang Zhang, Jiexi Liu, and Minlie Huang. 2019. A hierarchical framework for relation extraction with reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 7072–7079. find
  31. Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pages 142– 147. find
  32. Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, and Dawn Song. 2022. Deepstruct: Pre-training of language models for structure prediction. In Findings of the Association for Computational Linguistics: ACL 2022, pages 803–823. find
  33. Yucheng Wang, Bowen Yu, Yueyang Zhang, Tingwen Liu, Hongsong Zhu, and Limin Sun. 2020. TPLinker: Single-stage joint extraction of entities and relations through token pair linking. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1572–1582, Barcelona, Spain (On… find
  34. Zihan Wang, Jingbo Shang, Liyuan Liu, Lihao Lu, Jiacheng Liu, and Jiawei Han. 2019. Crossweigh: Training named entity tagger from imperfect annotations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Lan… find
  35. Zhepei Wei, Jianlin Su, Yue Wang, Yuan Tian, and Yi Chang. 2020a. A novel cascade binary tagging framework for relational triple extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1476– 1488. find
  36. Zhepei Wei, Jianlin Su, Yue Wang, Yuan Tian, and Yi Chang. 2020b. A novel cascade binary tagging framework for relational triple extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1476– 1488, Online. Association for Computational Linguistics… find
  37. Chenhao Xie, Jiaqing Liang, Jingping Liu, Chengsong Huang, Wenhao Huang, and Yanghua Xiao. 2021. Revisiting the negative data of distantly supervised relation extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confe… find
  38. Bowen Zhang, Daijun Ding, and Liwen Jing. 2022. How would stance detection techniques evolve after the launch of chatgpt? arXiv preprint arXiv:2212.14548. find
  39. Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, and Yefeng Zheng. 2021. Prgc: Potential relation and global correspondence based joint relational triple extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational… find
  40. Zexuan Zhong and Danqi Chen. 2021. A frustratingly easy approach for entity and relation extraction. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 50–61, Online. Association for Computational L… find our card
  41. Wenxuan Zhou and Muhao Chen. 2021. Learning from noisy labels for entity-centric information extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). find
  42. Enwei Zhu and Jinpeng Li. 2022. Boundary smoothing for named entity recognition. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7096–7108, Dublin, Ireland. Association for Computational Linguistics. find
  43. References Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag. 2022. Large language models are zero-shot clinical information extractors. arXiv preprint arXiv:2205.12689. find
  44. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901. find
  45. Lingjiao Chen, Matei Zaharia, and James Zou. 2023. How is chatgpt’s behavior changing over time? arXiv preprint arXiv:2307.09009. — [arXiv:2307.09009](https://arxiv.org/abs/2307.09009) find
  46. Xinya Du and Claire Cardie. 2020. Event extraction by answering (almost) natural questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 671– 683. find our card
  47. Matthew R Gormley, Mo Yu, and Mark Dredze. 2015. Improved relation extraction with feature-rich compositional embedding models. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1774–1784. find
  48. Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu. 2023. How close is chatgpt to human experts? comparison corpus, evaluation, and detection. arXiv preprint arxiv:2301.07597. find our card
  49. Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018. FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4803–480… find
  50. Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S Weld. 2011. Knowledge-based weak supervision for information extraction of overlapping relations. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pages 5… find
  51. Katharina Jeblick, Balthasar Schachtner, Jakob Dexl, Andreas Mittermeier, Anna Theresa Stüber, Johanna Topalis, Tobias Weber, Philipp Wesp, Bastian Oliver Sabel, Jens Ricke, et al. 2023. Chatgpt makes medicine easy to swallow: An exploratory case study on simplified radiology reports. European Radio… find
  52. Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, and Zhaopeng Tu. 2023. Is chatgpt a good translator? a preliminary study. arXiv preprint arXiv:2301.08745. find our card
  53. Michael R King. 2022. The future of ai in medicine: a perspective from a chatbot. Annals of Biomedical Engineering, pages 1–5. find
  54. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199– 22213. find our card
  55. Gina-Anne Levow. 2006. The third international Chinese language processing bakeoff: Word segmentation and named entity recognition. In Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, pages 108–117, Sydney, Australia. Association for Computational Linguistics. find
  56. Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, and Yong Zhu. 2020a. Event extraction as multi-turn question answering. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 829–838. find
  57. Jingye Li, Hao Fei, Jiang Liu, Shengqiong Wu, Meishan Zhang, Chong Teng, Donghong Ji, and Fei Li. 2021. Unified named entity recognition as word-word relation classification. ArXiv, abs/2112.10070. — [arXiv:2112.10070](https://arxiv.org/abs/2112.10070) find
  58. Shuangjie Li, Wei He, Yabing Shi, Wenbin Jiang, Haijin Liang, Ye Jiang, Yang Zhang, Yajuan Lyu, and Yong Zhu. 2019a. Duie: A large-scale chinese dataset for information extraction. In CCF International Conference on Natural Language Processing and Chinese Computing, pages 791–800. Springer. find
  59. Xiaoya Li, Xiaofei Sun, Yuxian Meng, Junjun Liang, Fei Wu, and Jiwei Li. 2020b. Dice loss for data-imbalanced NLP tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 465–476, Online. Association for Computational Linguistics. find
  60. Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, and Jiwei Li. 2019b. Entity-relation extraction as multi-turn question answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1340–1350. find
  61. Xinyu Li, Fayuan Li, Lu Pan, Yuguang Chen, Weihua Peng, Quan Wang, Yajuan Lyu, and Yong Zhu. 2020c. Duee: a large-scale dataset for chinese event extraction in real-world scenarios. In CCF International Conference on Natural Language Processing and Chinese Computing, pages 534–545. Springer. find
  62. Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, and Shaoyi Chen. 2021. Text2event: Controllable sequence-to-structure generation for end-to-end event extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the… find our card
  63. Yaojie Lu, Qing Liu, Dai Dai, Xinyan Xiao, Hongyu Lin, Xianpei Han, Le Sun, and Hua Wu. 2022. Unified structure generation for universal information extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5755–5772, Dublin… find
  64. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744. find
  65. Lev Ratinov and Dan Roth. 2009. Design challenges and misconceptions in named entity recognition. In Proceedings of the thirteenth conference on computational natural language learning (CoNLL-2009), pages 147–155. find our card
  66. Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010. Modeling relations and their mentions without labeled text. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 148–163. Springer. find
  67. Oscar Sainz, Itziar Gonzalez-Dios, Oier Lopez de Lacalle, Bonan Min, and Eneko Agirre. 2022a. Textual entailment for event argument extraction: Zero-and few-shot with multi-source learning. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2439–2455, Seattle, United Sta… find
  68. Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, Ander Barrena, and Eneko Agirre. 2021. Label verbalization and entailment for effective zero and few-shot relation extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1199–1212, Online and Punt… find
  69. Oscar Sainz, Haoling Qiu, Oier Lopez de Lacalle, Eneko Agirre, and Bonan Min. 2022b. ZS4IE: A toolkit for zero-shot information extraction with simple verbalizations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language… find
  70. Sunita Sarawagi et al. 2008. Information extraction. Foundations and Trends® in Databases, 1(3):261– 377. find
  71. Teo Susnjak. 2022. Chatgpt: The end of online exam integrity? arXiv preprint arXiv:2212.09292. — [arXiv:2212.09292](https://arxiv.org/abs/2212.09292) find
  72. Ryuichi Takanobu, Tianyang Zhang, Jiexi Liu, and Minlie Huang. 2019. A hierarchical framework for relation extraction with reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 7072–7079. find
  73. Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pages 142– 147. find
  74. Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, and Dawn Song. 2022. Deepstruct: Pre-training of language models for structure prediction. In Findings of the Association for Computational Linguistics: ACL 2022, pages 803–823. find
  75. Yucheng Wang, Bowen Yu, Yueyang Zhang, Tingwen Liu, Hongsong Zhu, and Limin Sun. 2020. TPLinker: Single-stage joint extraction of entities and relations through token pair linking. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1572–1582, Barcelona, Spain (On… find
  76. Zihan Wang, Jingbo Shang, Liyuan Liu, Lihao Lu, Jiacheng Liu, and Jiawei Han. 2019. Crossweigh: Training named entity tagger from imperfect annotations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Lan… find
  77. Zhepei Wei, Jianlin Su, Yue Wang, Yuan Tian, and Yi Chang. 2020a. A novel cascade binary tagging framework for relational triple extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1476– 1488. find
  78. Zhepei Wei, Jianlin Su, Yue Wang, Yuan Tian, and Yi Chang. 2020b. A novel cascade binary tagging framework for relational triple extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1476– 1488, Online. Association for Computational Linguistics… find
  79. Chenhao Xie, Jiaqing Liang, Jingping Liu, Chengsong Huang, Wenhao Huang, and Yanghua Xiao. 2021. Revisiting the negative data of distantly supervised relation extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confe… find
  80. Bowen Zhang, Daijun Ding, and Liwen Jing. 2022. How would stance detection techniques evolve after the launch of chatgpt? arXiv preprint arXiv:2212.14548. find
  81. Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, and Yefeng Zheng. 2021. Prgc: Potential relation and global correspondence based joint relational triple extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational… find
  82. Zexuan Zhong and Danqi Chen. 2021. A frustratingly easy approach for entity and relation extraction. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 50–61, Online. Association for Computational L… find our card
  83. Wenxuan Zhou and Muhao Chen. 2021. Learning from noisy labels for entity-centric information extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). find
  84. Enwei Zhu and Jinpeng Li. 2022. Boundary smoothing for named entity recognition. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7096–7108, Dublin, Ireland. Association for Computational Linguistics. find