{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:54:18Z","timestamp":1770749658140,"version":"3.50.0"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"The youth program of National Science Fund of Tianjin, China","award":["22JCQNJC01340"],"award-info":[{"award-number":["22JCQNJC01340"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3672067","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"83-94","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards Robust Information Extraction via Binomial Distribution Guided Counterpart Sequence"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9697-3028","authenticated-orcid":false,"given":"Yinhao","family":"Bai","sequence":"first","affiliation":[{"name":"College of Software, Nankai University & Tianjin Key Laboratory of Software Experience and Human Computer Interaction, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5160-2859","authenticated-orcid":false,"given":"Yuhua","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Software, Nankai University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4710-8019","authenticated-orcid":false,"given":"Zhixin","family":"Han","sequence":"additional","affiliation":[{"name":"College of Software, Nankai University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4797-3072","authenticated-orcid":false,"given":"Hang","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9507-9991","authenticated-orcid":false,"given":"Chao","family":"Xue","sequence":"additional","affiliation":[{"name":"JD Explore Academy, Beijing, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1536-5400","authenticated-orcid":false,"given":"Mengting","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Software, Nankai University & Tianjin Key Laboratory of Software Experience and Human Computer Interaction, Tianjin, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Proceedings of the Third Conference on Applied Natural Language Processing","author":"Andersen Peggy M.","unstructured":"Peggy M. Andersen, Philip J. Hayes, Alison K. Huettner, Linda M. Schmandt, Irene B. Nirenburg, and Steven P. Weinstein. 1992. Automatic Extraction of Facts from Press Releases to Generate News Stories. In Proceedings of the Third Conference on Applied Natural Language Processing (Trento, Italy) (ANLC '92). Association for Computational Linguistics, USA, 170--177. https:\/\/doi.org\/10. 3115\/974499.974531"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-linguistics-011619-030303"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.29"},{"key":"e_1_3_2_2_4_1","volume-title":"International conference on machine learning. PMLR, 1597--1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607."},{"key":"e_1_3_2_2_5_1","volume-title":"Template-based named entity recognition using BART. arXiv preprint arXiv:2106.01760","author":"Cui Leyang","year":"2021","unstructured":"Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang. 2021. Template-based named entity recognition using BART. arXiv preprint arXiv:2106.01760 (2021)."},{"key":"e_1_3_2_2_6_1","volume-title":"Proceedings of the AAAI conference on artificial intelligence. 6300--6308","author":"Dai Dai","year":"2019","unstructured":"Dai Dai, Xinyan Xiao, Yajuan Lyu, Shan Dou, Qiaoqiao She, and Haifeng Wang. 2019. Joint extraction of entities and overlapping relations using positionattentive sequence labeling. In Proceedings of the AAAI conference on artificial intelligence. 6300--6308. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/4591"},{"key":"e_1_3_2_2_7_1","volume-title":"Proceedings of the 3rd Workshop on Noisy User-generated Text. Association for Computational Linguistics","author":"Derczynski Leon","year":"2017","unstructured":"Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham. 2017. Results of theWNUT2017 Shared Task on Novel and Emerging Entity Recognition. In Proceedings of the 3rd Workshop on Noisy User-generated Text. Association for Computational Linguistics, Copenhagen, Denmark, 140--147. https:\/\/doi.org\/10. 18653\/v1\/W17--4418"},{"key":"e_1_3_2_2_8_1","volume-title":"Unified language model pretraining for natural language understanding and generation. Advances in neural information processing systems 32","author":"Dong Li","year":"2019","unstructured":"Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019. Unified language model pretraining for natural language understanding and generation. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1259"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1"},{"key":"e_1_3_2_2_11_1","volume-title":"55th annual meeting of the Association for Computational Linguistics (ACL). https: \/\/inria.hal.science\/hal-01623744\/document","author":"Gardent Claire","unstructured":"Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017. Creating training corpora for nlg micro-planning. In 55th annual meeting of the Association for Computational Linguistics (ACL). https: \/\/inria.hal.science\/hal-01623744\/document"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.113"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.240"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1017\/S1351324919000512"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3568160.3570233"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.538"},{"key":"e_1_3_2_2_17_1","unstructured":"Mengting Hu Zhen Zhang Shiwan Zhao Minlie Huang and Bingzhe Wu. 2023. Uncertainty in Natural Language Processing: Sources Quantification and Applications. arXiv:2306.04459 [cs.CL]"},{"key":"e_1_3_2_2_18_1","volume-title":"Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation. arXiv:2302.09664 [cs.CL]","author":"Kuhn Lorenz","year":"2023","unstructured":"Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2023. Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation. arXiv:2302.09664 [cs.CL]"},{"key":"e_1_3_2_2_19_1","volume-title":"Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360","author":"Lample Guillaume","year":"2016","unstructured":"Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1145"},{"key":"e_1_3_2_2_21_1","volume-title":"Multitask deep neural networks for natural language understanding. arXiv preprint arXiv:1901.11504","author":"Liu Xiaodong","year":"2019","unstructured":"Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019. Multitask deep neural networks for natural language understanding. arXiv preprint arXiv:1901.11504 (2019)."},{"key":"e_1_3_2_2_22_1","volume-title":"Universal Information Extraction as Unified Semantic Matching. arXiv preprint arXiv:2301.03282","author":"Lou Jie","year":"2023","unstructured":"Jie Lou, Yaojie Lu, Dai Dai, Wei Jia, Hongyu Lin, Xianpei Han, Le Sun, and Hua Wu. 2023. Universal Information Extraction as Unified Semantic Matching. arXiv preprint arXiv:2301.03282 (2023)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.395"},{"key":"e_1_3_2_2_24_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence. 8600--8607","author":"Peng Haiyun","year":"2020","unstructured":"Haiyun Peng, Lu Xu, Lidong Bing, Fei Huang,Wei Lu, and Luo Si. 2020. Knowing what, howand why: A near complete solution for aspect-based sentiment analysis. In Proceedings of the AAAI Conference on Artificial Intelligence. 8600--8607. https: \/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/6383"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S16-1002"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S15-2082"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Maria Pontiki Dimitris Galanis Ioannis Pavlopoulos Harris Papageorgiou Ion Androutsopoulos and Suresh Manandhar. 2014. SemEval 2014 Task 4: Aspect Based Sentiment Analysis. (2014).","DOI":"10.3115\/v1\/S14-2004"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/3455716.3455856"},{"key":"e_1_3_2_2_29_1","first-page":"1","article-title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research (JMLR) 21, 140 (2020), 1--67. http:\/\/jmlr.org\/papers\/v21\/20-074.html","journal-title":"Journal of Machine Learning Research (JMLR)"},{"key":"e_1_3_2_2_30_1","volume-title":"Proceedings of the eighth conference on computational natural language learning (CoNLL-2004)","author":"Roth Dan","year":"2004","unstructured":"Dan Roth and Wen-tau Yih. 2004. A linear programming formulation for global inference in natural language tasks. In Proceedings of the eighth conference on computational natural language learning (CoNLL-2004) at HLT-NAACL 2004. 1--8. https:\/\/aclanthology.org\/W04--2401.pdf"},{"key":"e_1_3_2_2_31_1","volume-title":"Arun Raja, et al.","author":"Sanh Victor","year":"2021","unstructured":"Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2021. Multitask prompted training enables zero-shot task generalization. arXiv preprint arXiv:2110.08207 (2021)."},{"key":"e_1_3_2_2_32_1","volume-title":"A simple but tough-to-beat data augmentation approach for natural language understanding and generation. arXiv preprint arXiv:2009.13818","author":"Shen Dinghan","year":"2020","unstructured":"Dinghan Shen, Mingzhi Zheng, Yelong Shen, Yanru Qu, and Weizhu Chen. 2020. A simple but tough-to-beat data augmentation approach for natural language understanding and generation. arXiv preprint arXiv:2009.13818 (2020)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1309"},{"key":"e_1_3_2_2_34_1","volume-title":"Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT). The COLING 2016 Organizing Committee","author":"Strauss Benjamin","year":"2016","unstructured":"Benjamin Strauss, Bethany Toma, Alan Ritter, Marie-Catherine de Marneffe, and Wei Xu. 2016. Results of the WNUT16 Named Entity Recognition Shared Task. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT). The COLING 2016 Organizing Committee, Osaka, Japan, 138--144. https:\/\/ aclanthology.org\/W16--3919"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1"},{"key":"e_1_3_2_2_36_1","volume-title":"CLEVE: contrastive pre-training for event extraction. arXiv preprint arXiv:2105.14485","author":"Wang Ziqi","year":"2021","unstructured":"Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, and Jie Zhou. 2021. CLEVE: contrastive pre-training for event extraction. arXiv preprint arXiv:2105.14485 (2021)."},{"key":"e_1_3_2_2_37_1","volume-title":"A learning algorithm for continually running fully recurrent neural networks. Neural computation 1, 2","author":"Williams Ronald J","year":"1989","unstructured":"Ronald J Williams and David Zipser. 1989. A learning algorithm for continually running fully recurrent neural networks. Neural computation 1, 2 (1989), 270--280. https:\/\/direct.mit.edu\/neco\/article-abstract\/1\/2\/270\/5490\/A-Learning-Algorithm-for-Continually-Running-Fully"},{"key":"e_1_3_2_2_38_1","volume-title":"Joint extraction of entities and relations based on a novel decomposition strategy. arXiv preprint arXiv:1909.04273","author":"Yu Bowen","year":"2019","unstructured":"Bowen Yu, Zhenyu Zhang, Xiaobo Shu, Yubin Wang, Tingwen Liu, Bin Wang, and Sujian Li. 2019. Joint extraction of entities and relations based on a novel decomposition strategy. arXiv preprint arXiv:1909.04273 (2019). https:\/\/ebooks. iospress.nl\/doi\/10.3233\/FAIA200356"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3594243"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.726"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-short.64"},{"key":"e_1_3_2_2_42_1","volume-title":"E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition. arXiv preprint arXiv:2305.17854","author":"Zhang Zhen","year":"2023","unstructured":"Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, and BingzheWu. 2023. E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition. arXiv preprint arXiv:2305.17854 (2023)."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1113"},{"key":"e_1_3_2_2_44_1","volume-title":"Joint extraction of entities and relations based on a novel tagging scheme. arXiv preprint arXiv:1706.05075","author":"Zheng Suncong","year":"2017","unstructured":"Suncong Zheng, FengWang, Hongyun Bao, Yuexing Hao, Peng Zhou, and Bo Xu. 2017. Joint extraction of entities and relations based on a novel tagging scheme. arXiv preprint arXiv:1706.05075 (2017)."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3672067","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3672067","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:23Z","timestamp":1750291463000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3672067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":44,"alternative-id":["10.1145\/3637528.3672067","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3672067","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}