{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:43:26Z","timestamp":1770752606286,"version":"3.50.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031390586","type":"print"},{"value":"9783031390593","type":"electronic"}],"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\/978-3-031-39059-3_13","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T13:01:37Z","timestamp":1690722097000},"page":"189-208","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Explaining Relation Classification Models with\u00a0Semantic Extents"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0931-8977","authenticated-orcid":false,"given":"Lars","family":"Kl\u00f6ser","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8298-2567","authenticated-orcid":false,"given":"Andre","family":"B\u00fcsgen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5972-8413","authenticated-orcid":false,"given":"Philipp","family":"Kohl","sequence":"additional","affiliation":[]},{"given":"Bodo","family":"Kraft","sequence":"additional","affiliation":[]},{"given":"Albert","family":"Z\u00fcndorf","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"13_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-031-01333-1_2","volume-title":"Advances in Intelligent Data Analysis XX","author":"H Ayats","year":"2022","unstructured":"Ayats, H., Cellier, P., Ferr\u00e9, S.: A two-step approach for explainable relation extraction. In: Bouadi, T., Fromont, E., H\u00fcllermeier, E. (eds.) IDA 2022. LNCS, vol. 13205, pp. 14\u201325. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-01333-1_2"},{"key":"13_CR2","unstructured":"Bolukbasi, T., Chang, K.W., Zou, J., Saligrama, V., Kalai, A.: Man is to computer programmer as woman is to homemaker? Debiasing word embeddings (2016)"},{"key":"13_CR3","unstructured":"Brunet, M.E., Alkalay-Houlihan, C., Anderson, A., Zemel, R.: Understanding the origins of bias in word embeddings. ArXiv (2018)"},{"key":"13_CR4","doi-asserted-by":"publisher","unstructured":"B\u00fcsgen, A., Kl\u00f6ser, L., Kohl, P., Schmidts, O., Kraft, B., Z\u00fcndorf, A.: Exploratory analysis of chat-based black market profiles with natural language processing. In: Proceedings of the 11th International Conference on Data Science, Technology and Applications, pp. 83\u201394. SCITEPRESS - Science and Technology Publications, Lisbon (2022). https:\/\/doi.org\/10.5220\/0011271400003269","DOI":"10.5220\/0011271400003269"},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"Clark, K., Khandelwal, U., Levy, O., Manning, C.D.: What does BERT look at? An analysis of BERT\u2019s attention (2019). https:\/\/doi.org\/10.48550\/arXiv.1906.04341","DOI":"10.48550\/arXiv.1906.04341"},{"key":"13_CR6","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 [cs] (2019)"},{"key":"13_CR7","unstructured":"Doddington, G., Mitchell, A., Przybocki, M., Ramshaw, L., Strassel, S., Weischedel, R.: The automatic content extraction (ACE) program tasks, data, and evaluation. In: Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC 2004). European Language Resources Association (ELRA), Lissabon (2004)"},{"key":"13_CR8","doi-asserted-by":"publisher","unstructured":"D\u2019Souza, J., Auer, S., Pedersen, T.: SemEval-2021 task 11: NLPContributionGraph - structuring scholarly NLP contributions for a research knowledge graph. In: Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pp. 364\u2013376. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.semeval-1.44","DOI":"10.18653\/v1\/2021.semeval-1.44"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Ebrahimi, J., Rao, A., Lowd, D., Dou, D.: HotFlip: white-box adversarial examples for text classification. arXiv:1712.06751 [cs] (2018)","DOI":"10.18653\/v1\/P18-2006"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Feng, S., Wallace, E., Ii, A.G., Iyyer, M., Rodriguez, P., Boyd-Graber, J.L.: Pathologies of neural models make interpretations difficult. Undefined (2018)","DOI":"10.18653\/v1\/D18-1407"},{"key":"13_CR11","doi-asserted-by":"publisher","unstructured":"Gardner, M., et al.: Evaluating models\u2019 local decision boundaries via contrast sets. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 1307\u20131323. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.117","DOI":"10.18653\/v1\/2020.findings-emnlp.117"},{"key":"13_CR12","doi-asserted-by":"publisher","unstructured":"Kl\u00f6ser, L., Kohl, P., Kraft, B., Z\u00fcndorf, A.: Multi-attribute relation extraction (MARE) - simplifying the application of relation extraction. In: Proceedings of the 2nd International Conference on Deep Learning Theory and Applications, pp. 148\u2013156 (2021). https:\/\/doi.org\/10.5220\/0010559201480156","DOI":"10.5220\/0010559201480156"},{"key":"13_CR13","unstructured":"Li, B., et al.: Detecting gender bias in transformer-based models: a case study on BERT. ArXiv (2021)"},{"key":"13_CR14","doi-asserted-by":"publisher","unstructured":"Li, Q., Ji, H.: Incremental joint extraction of entity mentions and relations. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 402\u2013412. Association for Computational Linguistics, Baltimore (2014). https:\/\/doi.org\/10.3115\/v1\/P14-1038","DOI":"10.3115\/v1\/P14-1038"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"McCoy, R.T., Pavlick, E., Linzen, T.: Right for the wrong reasons: diagnosing syntactic heuristics in natural language inference (2019)","DOI":"10.18653\/v1\/P19-1334"},{"key":"13_CR16","unstructured":"Meng, K., Bau, D., Andonian, A., Belinkov, Y.: Locating and editing factual associations in GPT (2023)"},{"issue":"2","key":"13_CR17","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1162\/coli_a_00379","volume":"46","author":"M Nissim","year":"2020","unstructured":"Nissim, M., van Noord, R., van der Goot, R.: Fair is better than sensational: man is to doctor as woman is to doctor. Comput. Linguist. 46(2), 487\u2013497 (2020)","journal-title":"Comput. Linguist."},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Wu, T., Guestrin, C., Singh, S.: Beyond accuracy: behavioral testing of NLP models with CheckList (2020)","DOI":"10.24963\/ijcai.2021\/659"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Schlangen, D.: Targeting the benchmark: on methodology in current natural language processing research. arXiv:2007.04792 [cs] (2020)","DOI":"10.18653\/v1\/2021.acl-short.85"},{"key":"13_CR20","doi-asserted-by":"publisher","unstructured":"Shahbazi, H., Fern, X., Ghaeini, R., Tadepalli, P.: Relation extraction with explanation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6488\u20136494. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.579","DOI":"10.18653\/v1\/2020.acl-main.579"},{"key":"13_CR21","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. CoRR (2013)"},{"key":"13_CR22","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F., Wattenberg, M.: SmoothGrad: removing noise by adding noise. arXiv:1706.03825 [cs, stat] (2017)"},{"key":"13_CR23","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. arXiv:1703.01365 [cs] (2017)"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Tenney, I., Das, D., Pavlick, E.: BERT rediscovers the classical NLP pipeline (2019)","DOI":"10.18653\/v1\/P19-1452"},{"key":"13_CR25","doi-asserted-by":"publisher","unstructured":"Wadden, D., Wennberg, U., Luan, Y., Hajishirzi, H.: Entity, relation, and event extraction with contextualized span representations. 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. 5784\u20135789. Association for Computational Linguistics, Hong Kong (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1585, https:\/\/aclanthology.org\/D19-1585","DOI":"10.18653\/v1\/D19-1585"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Wallace, E., Tuyls, J., Wang, J., Subramanian, S., Gardner, M., Singh, S.: AllenNLP interpret: a framework for explaining predictions of NLP models. arXiv:1909.09251 [cs] (2019)","DOI":"10.18653\/v1\/D19-3002"},{"key":"13_CR27","unstructured":"Wang, A., et al.: SuperGLUE: a stickier benchmark for general-purpose language understanding systems (2020)"},{"key":"13_CR28","doi-asserted-by":"publisher","unstructured":"Wu, Z., Chen, Y., Kao, B., Liu, Q.: Perturbed masking: parameter-free probing for analyzing and interpreting BERT (2020). https:\/\/doi.org\/10.18653\/v1\/P18-1198","DOI":"10.18653\/v1\/P18-1198"},{"key":"13_CR29","doi-asserted-by":"publisher","unstructured":"Yamada, I., Asai, A., Shindo, H., Takeda, H., Matsumoto, Y.: LUKE: deep contextualized entity representations with entity-aware self-attention. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6442\u20136454. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.523","DOI":"10.18653\/v1\/2020.emnlp-main.523"},{"key":"13_CR30","doi-asserted-by":"publisher","unstructured":"Zhang, B.H., Lemoine, B., Mitchell, M.: Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI\/ACM Conference on AI, Ethics, and Society, pp. 335\u2013340 (2018). https:\/\/doi.org\/10.1145\/3278721.3278779","DOI":"10.1145\/3278721.3278779"},{"issue":"2","key":"13_CR31","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1162\/dint_a_00014","volume":"1","author":"T Zhang","year":"2019","unstructured":"Zhang, T., Ji, H., Sil, A.: Joint entity and event extraction with generative adversarial imitation learning. Data Intell. 1(2), 99\u2013120 (2019)","journal-title":"Data Intell."},{"key":"13_CR32","doi-asserted-by":"publisher","unstructured":"Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Men also like shopping: reducing gender bias amplification using corpus-level constraints. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2979\u20132989. Association for Computational Linguistics, Copenhagen (2017). https:\/\/doi.org\/10.18653\/v1\/D17-1323","DOI":"10.18653\/v1\/D17-1323"},{"key":"13_CR33","unstructured":"Zhong, Q., et al.: Toward efficient language model pretraining and downstream adaptation via self-evolution: a case study on SuperGLUE (2022)"}],"container-title":["Communications in Computer and Information Science","Deep Learning Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39059-3_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T13:04:47Z","timestamp":1690722287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39059-3_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031390586","9783031390593"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39059-3_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DeLTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Deep Learning Theory and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"13 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"delta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/delta.scitevents.org\/","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":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","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":"9","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":"22","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":"21% - 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)"}}]}}