{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T06:29:59Z","timestamp":1750746599438,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031446955"},{"type":"electronic","value":"9783031446962"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44696-2_18","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T09:03:59Z","timestamp":1696669439000},"page":"223-235","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SSUIE 1.0: A Dataset for Chinese Space Science and Utilization Information Extraction"],"prefix":"10.1007","author":[{"given":"Yunfei","family":"Liu","sequence":"first","affiliation":[]},{"given":"Shengyang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiong","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Yifeng","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Linjie","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shiyi","family":"Hao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"key":"18_CR1","unstructured":"Huang, Z., Xu, W., Yu, K.: Bidirectional lstm-crf models for sequence tagging. arxiv:1508.01991 (2015)"},{"key":"18_CR2","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/978-3-319-50496-4_20","volume-title":"Natural Language Understanding and Intelligent Applications","author":"C Dong","year":"2016","unstructured":"Dong, C., Zhang, J., Zong, C., Hattori, M., Di, H.: Character-based lstm-crf with radical-level features for chinese named entity recognition. In: Lin, C.Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) Natural Language Understanding and Intelligent Applications, pp. 239\u2013250. Springer International Publishing, Cham (2016)"},{"key":"18_CR3","unstructured":"Yan, H., Deng, B., Li, X., Qiu, X.: Tener: adapting transformer encoder for named entity recognition. arxiv:1911.04474 (2019)"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Zhu, E., Li, J.: Boundary smoothing for named entity recognition. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Vol. 1, pp. 7096\u20137108. https:\/\/aclanthology.org\/2022.acl-long.490","DOI":"10.18653\/v1\/2022.acl-long.490"},{"key":"18_CR5","unstructured":"Yue, Z., Yang, J.: Chinese NER Using Lattice LSTM. arxiv:1805.02023 (2018)"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Liu, W., Xu, T., Xu, Q., Song, J., Zu, Y.: An encoding strategy based word-character LSTM for Chinese NER. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/N19-1247"},{"key":"18_CR7","doi-asserted-by":"publisher","unstructured":"Gui, T., Ma, R., Zhang, Q., Zhao, L., Jiang, Y.G., Huang, X.: Cnn-based chinese ner with lexicon rethinking. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. pp. 4982\u20134988. https:\/\/doi.org\/10.24963\/ijcai.2019\/692","DOI":"10.24963\/ijcai.2019\/692"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Sui, D., Chen, Y., Liu, K., Zhao, J., Liu, S.: Leverage lexical knowledge for Chinese named entity recognition via collaborative graph network. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp.\u00a0 3830\u20133840, Hong Kong, China\u00a0(2019)","DOI":"10.18653\/v1\/D19-1396"},{"key":"18_CR9","unstructured":"Peng, M., Ma, R., Zhang, Q., Huang, X.: Simplify the usage of lexicon in Chinese NER. Annual Meeting of the Association for Computational Linguistics\u00a0(2019)"},{"key":"18_CR10","doi-asserted-by":"publisher","unstructured":"Li, X., Yan, H., Qiu, X., Huang, X.: FLAT: Chinese NER using flat-lattice transformer. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6836\u20136842. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.611","DOI":"10.18653\/v1\/2020.acl-main.611"},{"key":"18_CR11","unstructured":"Wu, S., Song, X., Feng, Z., Wu, X.: Nflat: Non-flat-lattice transformer for chinese named entity recognition. arXiv:2205.05832 (2022)"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Liu, W., Fu, X., Zhang, Y., Xiao, W.: Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter. arxiv:2105.07148 (2021)","DOI":"10.18653\/v1\/2021.acl-long.454"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: Unified named entity recognition as word-word relation classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no 10, pp. 10965\u201310973 (2022)","DOI":"10.1609\/aaai.v36i10.21344"},{"key":"18_CR14","unstructured":"Yan, X., Lili, M., Ge, L., et al.: Classifying relations via long short term memory networks along shortest dependency paths. In: Proceedings of the. Conference on Empirical Methods in Natural Language Processing, vol. 2015, pp. 1785\u20131794 (2015)"},{"key":"18_CR15","unstructured":"Zexuan, Z., Chen, D.: A frustratingly easy approach for entity and relation extraction. arxiv:2010.12812 (2020)"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Wei, Z., Su, J., Wang, Y., Tian, Y., Chang, Y.: A novel cascade binary tagging framework for relational triple extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1476\u20131488, Online. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.136"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yu, B., Zhang, Y., Liu, T., Zhu, H., Sun, L.: TPLinker: single-stage joint extraction of entities and relations through token pair linking. In: International Conference on Computational Linguistics\u00a0(2020)","DOI":"10.18653\/v1\/2020.coling-main.138"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Yan, Z., Zhang, C., Fu, J., Zhang, Q., Wei, Z.: A partition filter network for joint entity and relation extraction. In: Conference on Empirical Methods in Natural Language Processing\u00a0(2021)","DOI":"10.18653\/v1\/2021.emnlp-main.17"},{"key":"18_CR19","unstructured":"Ji, H., Grishman, R.: Refining event extraction through cross-document inference. In: Proceedings of ACL-08: HLT, pp. 254\u2013262, Columbus, Ohio. Association for Computational Linguistics (2008)"},{"key":"18_CR20","unstructured":"Devlin J, Chang M W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: CLEVE: contrastive pre-training for event extraction. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 6283\u20136297, Online. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.acl-long.491"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Wei, K., Sun, X., Zhang, Z., Zhang, J.,\u00a0 Zhi, G., Jin, L.: Trigger is not sufficient: Exploiting frame-aware knowledge for implicit event argument extraction. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol. 1, pp. 4672\u20134682, Online. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.acl-long.360"},{"key":"18_CR23","unstructured":"Sun Y, Wang S, Feng S, et al. Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation. arXiv preprint arXiv:2107.02137 (2021)"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Li, S., Ji, H., Han, J.: Document-level event argument extraction by conditional generation. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 894\u2013908, Online. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.naacl-main.69"},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Ma, Y., Wang, Z., Caom Y., et al.: Prompt for extraction? paie: Prompting argument interaction for event argument extraction. arXiv preprint arXiv:2202.12109 (2022)","DOI":"10.18653\/v1\/2022.acl-long.466"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44696-2_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T09:06:38Z","timestamp":1696669598000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44696-2_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031446955","9783031446962"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44696-2_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Foshan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2023\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Softconf","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"478","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"143","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}