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While many works apply rules and\/or regular expressions to candidate sections within the text, we follow a question answering (QA) based approach to identify those passages that are most likely to inform us about funding. With regard to a digital library scenario, we are dealing with three more challenges: confirming that our approach at least outperforms manual indexing, disambiguation of funding organizations by linking their names to authority data, and integrating the generated metadata into a digital library application. Our computational results by means of machine learning techniques show that our QA performs similar to a previous work (AckNER), although we operated on rather small sets of training and test data. While manual indexing is still needed for a gold standard of reliable metadata, the identification of funding entities only worked for a subset of funder names.<\/jats:p>","DOI":"10.1007\/978-3-031-16802-4_24","type":"book-chapter","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T10:05:31Z","timestamp":1663149931000},"page":"289-296","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Extracting Funder Information from\u00a0Scientific Papers - Experiences with\u00a0Question Answering"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2481-029X","authenticated-orcid":false,"given":"Timo","family":"Borst","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5667-5048","authenticated-orcid":false,"given":"Jonas","family":"Mielck","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5287-1444","authenticated-orcid":false,"given":"Matthias","family":"Nannt","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2623-0106","authenticated-orcid":false,"given":"Wolfgang","family":"Riese","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"24_CR1","unstructured":"Alexander, D., de Vries, A.P.: This research is funded by...: named entity recognition of financial information in research papers (2021). http:\/\/ceur-ws.org\/Vol-2847\/paper-10.pdf, https:\/\/repository.ubn.ru.nl\/handle\/2066\/236372"},{"key":"24_CR2","doi-asserted-by":"publisher","unstructured":"Bian, J., Huang, L., Huang, X., Zhou, H., Zhu, S.: Grantrel: grant information extraction via joint entity and relation extraction. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 2674\u20132685 (2021). https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.236","DOI":"10.18653\/v1\/2021.findings-acl.236"},{"key":"24_CR3","doi-asserted-by":"publisher","unstructured":"Councill, I.G., Giles, C.L., Han, H., Manavoglu, E.: Automatic acknowledgement indexing: expanding the semantics of contribution in the CiteSeer digital library. In: Proceedings of the 3rd International Conference on Knowledge Capture, pp. 19\u201326. 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