{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:22:19Z","timestamp":1773789739700,"version":"3.50.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031345593","type":"print"},{"value":"9783031345609","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\/978-3-031-34560-9_28","type":"book-chapter","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T01:05:06Z","timestamp":1686099906000},"page":"471-486","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Bridging Research Fields: An Empirical Study on\u00a0Joint, Neural Relation Extraction Techniques"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6785-8998","authenticated-orcid":false,"given":"Lars","family":"Ackermann","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julian","family":"Neuberger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"K\u00e4ppel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Jablonski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"key":"28_CR1","unstructured":"van der Aa, H., Carmona, J., Leopold, H., Mendling, J., Padr\u00f3, L.: Challenges and opportunities of applying natural language processing in business process management. In: Proceedings of COLING. ACL (2018)"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Ackermann, L., Neuberger, J., Jablonski, S.: Data-driven annotation of textual process descriptions based on formal meaning representations. In: CAiSE. Springer International Publishing (2021)","DOI":"10.1007\/978-3-030-79382-1_5"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Alshuwaier, F., Areshey, A., Poon, J.: A comparative study of the current technologies and approaches of relation extraction in biomedical literature using text mining. In: ICETAS (2017)","DOI":"10.1109\/ICETAS.2017.8277841"},{"key":"28_CR4","unstructured":"Btoush, E.S., Hammad, M.M.: Generating er diagrams from requirement specifications based on natural language processing. In: IJDTA (2015)"},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"Dawood, O.S., et al.: From requirements engineering to uml using natural language processing-survey study. In: EJERS (2017)","DOI":"10.24018\/ejers.2017.2.1.236"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Eberts, M., Ulges, A.: An end-to-end model for entity-level relation extraction using multi-instance learning. In: EACL ACL (2021)","DOI":"10.18653\/v1\/2021.eacl-main.319"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Elallaoui, M., Nafil, K., Touahni, R.: Automatic transformation of user stories into uml use case diagrams using nlp techniques. Procedia Computer Science (2018)","DOI":"10.1016\/j.procs.2018.04.010"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Friedrich, F., Mendling, J., Puhlmann, F.: Process model generation from natural language text. In: CAiSE. Springer, Berlin Heidelberg (2011)","DOI":"10.1007\/978-3-642-21640-4_36"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Han, X., Yu, P., Liu, Z., Sun, M., Li, P.: Hierarchical relation extraction with coarse-to-fine grained attention. In: EMNLP. ACL (2018)","DOI":"10.18653\/v1\/D18-1247"},{"key":"28_CR10","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. In: EMNLP. ACL (2018)","DOI":"10.18653\/v1\/D18-1514"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Hendrickx, I., et al.: SemEval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. In: SemEval. ACL (2010)","DOI":"10.3115\/1621969.1621986"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Kl\u00f6ser, L., Kohl, P., Kraft, B., Z\u00fcndorf, A.: Multi-attribute relation extraction (MARE): simplifying the application of relation extraction. In: DeLTA. SCITEPRESS (2021)","DOI":"10.5220\/0010559201480156"},{"key":"28_CR13","unstructured":"Kumar, S.: A survey of deep learning methods for relation extraction. CoRR (2017)"},{"key":"28_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s11431-020-1673-6","volume-title":"A survey on neural relation extraction","author":"K Liu","year":"2020","unstructured":"Liu, K.: A survey on neural relation extraction. Sci. China Technol, Sciences (2020)"},{"key":"28_CR15","unstructured":"L\u00f3pez, H.A., Debois, S., Hildebrandt, T.T., Marquard, M.: The process highlighter: From texts to declarative processes and back. In: CEUR Workshop Proc. (2018)"},{"key":"28_CR16","volume-title":"Named entity recognition and relation extraction: State-of-the-art","author":"Z Nasar","year":"2021","unstructured":"Nasar, Z., Jaffry, S.W., Malik, M.K.: Named entity recognition and relation extraction: State-of-the-art. ACM Comput, Surv (2021)"},{"key":"28_CR17","volume-title":"Deep neural approaches to relation triplets extraction: a comprehensive survey","author":"T Nayak","year":"2021","unstructured":"Nayak, T., Majumder, N., Goyal, P., Poria, S.: Deep neural approaches to relation triplets extraction: a comprehensive survey. Cogn, Comput (2021)"},{"key":"28_CR18","unstructured":"Page, M.J., et al.: The prisma 2020 statement: an updated guideline for reporting systematic reviews. In: Systematic reviews (2021)"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Peng, H., et al.: Learning from Context or Names? An Empirical Study on Neural Relation Extraction. In: EMNLP. ACL (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.298"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Qian, K., et al.: Annotation inconsistency and entity bias in MultiWOZ. In: Proceedings of SIGdial. ACL (2021)","DOI":"10.18653\/v1\/2021.sigdial-1.35"},{"key":"28_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-030-58666-9_11","volume-title":"Business Process Management","author":"L Quishpi","year":"2020","unstructured":"Quishpi, L., Carmona, J., Padr\u00f3, L.: Extracting annotations from textual descriptions of processes. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 184\u2013201. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58666-9_11"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Ren, F., et al.: A novel global feature-oriented relational triple extraction model based on table filling. In: EMNLP. ACL (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.208"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Proceedings of ECML PKDD. Springer, Berlin Heidelberg (2010)","DOI":"10.1007\/978-3-642-15939-8_10"},{"key":"28_CR24","unstructured":"Roth, D., Yih, W.t.: A linear programming formulation for global inference in natural language tasks. In: CoNLL. ACL (2004)"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Salih Dawood, O., Sahraoui, A.E.K.: From requirements engineering to uml using natural language processing - survey study. EJIE (2017)","DOI":"10.24018\/ejers.2017.2.1.236"},{"key":"28_CR26","doi-asserted-by":"crossref","unstructured":"Saranya, M., Geetha, T.V., Annie, R.A.X.: Comparative analysis of different deep learning techniques for relation extraction from biomed\u0131cal literature. In: Proceedings of ICSADL. Springer Singapore (2022)","DOI":"10.1007\/978-981-16-5157-1_33"},{"key":"28_CR27","doi-asserted-by":"crossref","unstructured":"Taill\u00e9, B., Guigue, V., Scoutheeten, G., Gallinari, P.: Let\u2019s Stop Incorrect Comparisons in End-to-end Relation Extraction! In: EMNLP. ACL (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.301"},{"key":"28_CR28","doi-asserted-by":"crossref","unstructured":"Vashishth, S., Joshi, R., Prayaga, S.S., Bhattacharyya, C., Talukdar, P.: RESIDE: Improving distantly-supervised neural relation extraction using side information. In: EMNLP. ACL (2018)","DOI":"10.18653\/v1\/D18-1157"},{"key":"28_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06667-3","volume-title":"Deep neural network-based relation extraction: an overview","author":"H Wang","year":"2022","unstructured":"Wang, H., Qin, K., Zakari, R.Y., Lu, G., Yin, J.: Deep neural network-based relation extraction: an overview. Neural Comput, Appl (2022)"},{"key":"28_CR30","doi-asserted-by":"crossref","unstructured":"Wang, J., Lu, W.: Two are better than one: Joint entity and relation extraction with table-sequence encoders. In: EMNLP. ACL (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.133"},{"key":"28_CR31","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: ACL (2020)","DOI":"10.18653\/v1\/2020.acl-main.136"},{"key":"28_CR32","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: EMNLP. ACL (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.17"},{"key":"28_CR33","unstructured":"Yu, B., et al.: Joint extraction of entities and relations based on a novel decomposition strategy. In: Proceedings of ECAI (2020)"},{"key":"28_CR34","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Zhou, X., Pan, S., Zhu, Q., Song, Z., Guo, L.: A relation-specific attention network for joint entity and relation extraction. In: IJCAI (2020)","DOI":"10.24963\/ijcai.2020\/561"},{"key":"28_CR35","doi-asserted-by":"crossref","unstructured":"Zeng, D., Zhang, H., Liu, Q.: Copymtl: Copy mechanism for joint extraction of entities and relations with multi-task learning. ArXiv (2020)","DOI":"10.1609\/aaai.v34i05.6495"},{"key":"28_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, X., Dai, Y., Jiang, T.: A survey deep learning based relation extraction. J. Phys.: Conf. Series 1601, 032029 (2020)","DOI":"10.1088\/1742-6596\/1601\/3\/032029"}],"container-title":["Lecture Notes in Computer Science","Advanced Information Systems Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34560-9_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T17:45:04Z","timestamp":1757094304000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34560-9_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031345593","9783031345609"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34560-9_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAiSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zaragoza","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"35","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caise2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}