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Relational and Fine-Grained Argument Mining

Kurzanzeige
dc.contributor.authorTrautmann, Dietrich
dc.contributor.authorFromm, Michael
dc.contributor.authorTresp, Volker
dc.contributor.authorSeidl, Thomas
dc.contributor.authorSchütze, Hinrich
dc.date.accessioned2021-05-04T09:37:30Z
dc.date.available2021-05-04T09:37:30Z
dc.date.issued2020
dc.description.abstractIn 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.identifier.doi10.1007/s13222-020-00341-z
dc.identifier.pissn1610-1995
dc.identifier.urihttp://dx.doi.org/10.1007/s13222-020-00341-z
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/36397
dc.publisherSpringer
dc.relation.ispartofDatenbank-Spektrum: Vol. 20, No. 2
dc.relation.ispartofseriesDatenbank-Spektrum
dc.subjectArgument Mining
dc.subjectRelational Machine Learning
dc.subjectStance Classification
dc.titleRelational and Fine-Grained Argument Miningde
dc.typeText/Journal Article
gi.citation.endPage105
gi.citation.startPage99

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Datenbank Spektrum 20(2) - Juli 2020