| dc.contributor.author | Trautmann, Dietrich | |
| dc.contributor.author | Fromm, Michael | |
| dc.contributor.author | Tresp, Volker | |
| dc.contributor.author | Seidl, Thomas | |
| dc.contributor.author | Schütze, Hinrich | |
| dc.date | 2020-07-01 | |
| dc.date.accessioned | 2021-05-04T09:37:30Z | |
| dc.date.available | 2021-05-04T09:37:30Z | |
| dc.date.issued | 2020 | |
| dc.identifier.issn | 1610-1995 | |
| dc.identifier.uri | http://dx.doi.org/10.1007/s13222-020-00341-z | |
| dc.identifier.uri | http://dl.gi.de/handle/20.500.12116/36397 | |
| dc.description.abstract | In our project ReMLAV , funded within the DFG Priority Program RATIO ( http://www.spp-ratio.de/ ), we focus on relational and fine-grained argument mining. In this article, we first introduce the problems we address and then summarize related work. The main part of the article describes our research on argument mining, both coarse-grained and fine-grained methods, and on same-side stance classification, a relational approach to the problem of stance classification. We conclude with an outlook. | de |
| dc.publisher | Springer | |
| dc.relation.ispartof | Datenbank-Spektrum: Vol. 20, No. 2 | |
| dc.relation.ispartofseries | Datenbank-Spektrum | |
| dc.subject | Argument Mining | |
| dc.subject | Relational Machine Learning | |
| dc.subject | Stance Classification | |
| dc.title | Relational and Fine-Grained Argument Mining | de |
| dc.type | Text/Journal Article | |
| mci.reference.pages | 99-105 | |
| dc.identifier.doi | 10.1007/s13222-020-00341-z | |
