{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:36:54Z","timestamp":1742992614839,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466731"},{"type":"electronic","value":"9783031466748"}],"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\/978-3-031-46674-8_5","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"63-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Prototype Network with\u00a0Common and\u00a0Discriminative Representation Learning for\u00a0Few-Shot Relation Extraction"],"prefix":"10.1007","author":[{"given":"Wenyue","family":"Hu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5169-4634","authenticated-orcid":false,"given":"Jiang","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Yangmei","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Rongzhen","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"5_CR1","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, vol. 26 (2013)"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Brody, S., Wu, S., Benton, A.: Towards realistic few-shot relation extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 5338\u20135345 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.433"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Chen, M., Zhang, W., Zhang, W., Chen, Q., Chen, H.: Meta relational learning for few-shot link prediction in knowledge graphs. 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. 4217\u20134226 (2019)","DOI":"10.18653\/v1\/D19-1431"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, P., et al.: Improving disentangled text representation learning with information-theoretic guidance. arXiv preprint: arXiv:2006.00693 (2020)","DOI":"10.18653\/v1\/2020.acl-main.673"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Dong, B., et al.: Meta-information guided meta-learning for few-shot relation classification. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1594\u20131605 (2020)","DOI":"10.18653\/v1\/2020.coling-main.140"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Dong, M., Pan, C., Luo, Z.: MapRE: an effective semantic mapping approach for low-resource relation extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2694\u20132704 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.212"},{"key":"5_CR7","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126\u20131135. PMLR (2017)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Gao, T., Han, X., Liu, Z., Sun, M.: Hybrid attention-based prototypical networks for noisy few-shot relation classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6407\u20136414 (2019)","DOI":"10.1609\/aaai.v33i01.33016407"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Gao, T., et al.: FewRel 2.0: towards more challenging few-shot relation classification. arXiv preprint: arXiv:1910.07124 (2019)","DOI":"10.18653\/v1\/D19-1649"},{"key":"5_CR10","unstructured":"Garcia, V., Bruna, J.: Few-shot learning with graph neural networks. arXiv preprint: arXiv:1711.04043 (2017)"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Han, J., Cheng, B., Lu, W.: Exploring task difficulty for few-shot relation extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2605\u20132616 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.204"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Han, M., et al.: Not all instances contribute equally: Instance-adaptive class representation learning for few-shot visual recognition. IEEE Trans. Neural Netw. Learn. Syst. (2022)","DOI":"10.1109\/TNNLS.2022.3204684"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Han, X., et al.: FewRel: a large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. arXiv preprint: arXiv:1810.10147 (2018)","DOI":"10.18653\/v1\/D18-1514"},{"issue":"8","key":"5_CR14","doi-asserted-by":"publisher","first-page":"3458","DOI":"10.1109\/TNNLS.2020.3011526","volume":"32","author":"N Lai","year":"2020","unstructured":"Lai, N., Kan, M., Han, C., Song, X., Shan, S.: Learning to learn adaptive classifier-predictor for few-shot learning. IEEE Trans. Neural Netw. Learn. Syst. 32(8), 3458\u20133470 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Lee, W.Y., Wang, J.Y., Wang, Y.C.F.: Domain-agnostic meta-learning for cross-domain few-shot classification. In: ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1715\u20131719. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746025"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Li, W.H., Liu, X., Bilen, H.: Cross-domain few-shot learning with task-specific adapters. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7161\u20137170 (2022)","DOI":"10.1109\/CVPR52688.2022.00702"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Hu, J., Wan, X., Chang, T.H.: Learn from relation information: towards prototype representation rectification for few-shot relation extraction. In: Findings of the Association for Computational Linguistics: NAACL 2022, pp. 1822\u20131831 (2022)","DOI":"10.18653\/v1\/2022.findings-naacl.139"},{"key":"5_CR20","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint: arXiv:1711.05101 (2017)"},{"key":"5_CR21","unstructured":"Nasution, M.K.: Social network mining (SNM): a definition of relation between the resources and SNA. arXiv preprint: arXiv:2207.06234 (2022)"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Peng, H., et al.: Learning from context or names? An empirical study on neural relation extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 3661\u20133672 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.298"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Popovic, N., F\u00e4rber, M.: Few-shot document-level relation extraction. arXiv preprint: arXiv:2205.02048 (2022)","DOI":"10.18653\/v1\/2022.naacl-main.421"},{"key":"5_CR24","unstructured":"Qu, M., Gao, T., Xhonneux, L.P., Tang, J.: Few-shot relation extraction via Bayesian meta-learning on relation graphs. In: International Conference on Machine Learning, pp. 7867\u20137876. PMLR (2020)"},{"issue":"7","key":"5_CR25","doi-asserted-by":"publisher","first-page":"e102039","DOI":"10.1371\/journal.pone.0102039","volume":"9","author":"C Quan","year":"2014","unstructured":"Quan, C., Wang, M., Ren, F.: An unsupervised text mining method for relation extraction from biomedical literature. PLoS ONE 9(7), e102039 (2014)","journal-title":"PLoS ONE"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Ren, H., Cai, Y., Chen, X., Wang, G., Li, Q.: A two-phase prototypical network model for incremental few-shot relation classification. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1618\u20131629 (2020)","DOI":"10.18653\/v1\/2020.coling-main.142"},{"issue":"5","key":"5_CR27","first-page":"4852","volume":"35","author":"H Ren","year":"2022","unstructured":"Ren, H., Cai, Y., Lau, R.Y.K., Leung, H.F., Li, Q.: Granularity-aware area prototypical network with bimargin loss for few shot relation classification. IEEE Trans. Knowl. Data Eng. 35(5), 4852\u20134866 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Simon, C., Koniusz, P., Nock, R., Harandi, M.: Adaptive subspaces for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4136\u20134145 (2020)","DOI":"10.1109\/CVPR42600.2020.00419"},{"key":"5_CR29","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"5_CR30","doi-asserted-by":"publisher","unstructured":"Tran, V.H., Ouchi, H., Watanabe, T., Matsumoto, Y.: Improving discriminative learning for zero-shot relation extraction. In: Proceedings of the 1st Workshop on Semiparametric Methods in NLP: Decoupling Logic from Knowledge, pp. 1\u20136. Association for Computational Linguistics, Dublin, Ireland and Online (2022). https:\/\/doi.org\/10.18653\/v1\/2022.spanlp-1.1, https:\/\/aclanthology.org\/2022.spanlp-1.1","DOI":"10.18653\/v1\/2022.spanlp-1.1"},{"key":"5_CR31","unstructured":"Wang, M., Zheng, J., Cai, F., Shao, T., Chen, H.: DRK: discriminative rule-based knowledge for relieving prediction confusions in few-shot relation extraction. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 2129\u20132140 (2022)"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Wang, Y., Salamon, J., Bryan, N.J., Bello, J.P.: Few-shot sound event detection. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 81\u201385. IEEE (2020)","DOI":"10.1109\/ICASSP40776.2020.9054708"},{"key":"5_CR33","unstructured":"Xiao, Y., Jin, Y., Hao, K.: Adaptive prototypical networks with label words and joint representation learning for few-shot relation classification. IEEE Trans. Neural Netw. Learn. Syst. (2021)"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Yang, K., Zheng, N., Dai, X., He, L., Huang, S., Chen, J.: Enhance prototypical network with text descriptions for few-shot relation classification. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2273\u20132276 (2020)","DOI":"10.1145\/3340531.3412153"},{"key":"5_CR35","doi-asserted-by":"crossref","unstructured":"Yang, S., Zhang, Y., Niu, G., Zhao, Q., Pu, S.: Entity concept-enhanced few-shot relation extraction. arXiv preprint: arXiv:2106.02401 (2021)","DOI":"10.18653\/v1\/2021.acl-short.124"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Ye, Z.X., Ling, Z.H.: Multi-level matching and aggregation network for few-shot relation classification. arXiv preprint: arXiv:1906.06678 (2019)","DOI":"10.18653\/v1\/P19-1277"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46674-8_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:15:48Z","timestamp":1699103748000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46674-8_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466731","9783031466748"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46674-8_5","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":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","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":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","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":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","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":"216","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":"43% - 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":"2.97","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":"3.77","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)"}}]}}