| dc.date.accessioned | 2020-10-26T14:36:13Z | |
| dc.date.available | 2020-10-26T14:36:13Z | |
| dc.date.issued | 2020 | |
| dc.identifier | doi:10.17170/kobra-202010231995 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/11899 | |
| dc.language.iso | eng | eng |
| dc.publisher | GITO Verlag | |
| dc.rights | Urheberrechtlich geschützt | |
| dc.rights.uri | https://rightsstatements.org/page/InC/1.0/ | |
| dc.subject | argumentation mining | eng |
| dc.subject | argument identification | eng |
| dc.subject | transfer learning | eng |
| dc.subject | natural language processing | eng |
| dc.subject.ddc | 004 | |
| dc.subject.ddc | 300 | |
| dc.title | Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach | eng |
| dc.type | Konferenzveröffentlichung | |
| dcterms.abstract | Argument identification is the fundamental block of every Argumentation Mining pipeline, which in turn is a young upcoming field with multiple applications ranging from strategy support to opinion mining and news fact-checking. We developed a model, which is tackling the two biggest practical and academic challenges of the research field today. First, it addresses the lack of corpus-agnostic models and, second, it tackles the problem of human-labor-intensive NLP models being costly to develop. We do that by suggesting and implementing an easy-to-use solution that utilizes the latest advancements in natural language Transfer Learning. The result is a two-fold contribution: A system that delivers state-of-the-art results in multiple corpora and opens up a new way of academic advancement of the field through Transfer Learning. Additionally, it provides the architecture for an easy-to-use tool that can be used for practical applications without the need for domain-specific knowledge. | eng |
| dcterms.accessRights | open access | |
| dcterms.creator | Wambsganss, Thiemo | |
| dcterms.creator | Molyndris, Nikolaos | |
| dcterms.creator | Söllner, Matthias | |
| dc.publisher.place | Berlin | |
| dc.relation.doi | doi:10.30844/wi_2020_c9-wambsganss | |
| dc.subject.swd | Argument | ger |
| dc.subject.swd | Identifikation | ger |
| dc.subject.swd | Transfer | ger |
| dc.subject.swd | Lernen | ger |
| dc.subject.swd | Natürlichsprachiges System | ger |
| dc.type.version | publishedVersion | |
| dcterms.event.date | 2020-03 | |
| dcterms.event.place | Potsdam | |
| dcterms.source.collection | Entwicklungen, Chancen und Herausforderungen der Digitalisierung. Band 1: Proceedings der 15. Internationalen Tagung Wirtschaftsinformatik 2020 | ger |
| dcterms.source.editor | Gronau, Norbert | |
| dcterms.source.editor | Heine, Moreen | |
| dcterms.source.editor | Krasnova, Hanna | |
| dcterms.source.editor | Pousttchi, Key | |
| dcterms.source.identifier | ISBN 978-3-95545-335-0 | |
| dcterms.source.pageinfo | 341-356 | |
| kup.iskup | false | |
| dcterms.event | 15. Internationalen Tagung Wirtschaftsinformatik 2020 | ger |
