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1
Come hither or go away? Recognising pre-electoral coalition signals in the news ...
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2
Come hither or go away? Recognising pre-electoral coalition signals in the news
Rehbein, Ines; Ponzetto, Simone Paolo; Adendorf, Anna. - : Association for Computational Linguistics, 2021
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3
Predicting Modality in Financial Dialogue ...
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4
Exploring Morality in Argumentation ...
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5
Knowledge Graphs meet Moral Values ...
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6
Unsupervised stance detection for arguments from consequences
Kobbe, Jonathan; Stuckenschmidt, Heiner; Hulpus, Ioana. - : Association for Computational Linguistics, 2020
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7
Predicting modality in financial dialogue
Stuckenschmidt, Heiner; Theil, Christoph Kilian. - : Association for Computational Linguistics, 2020
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8
Exploring morality in argumentation
Hulpus, Ioana; Stuckenschmidt, Heiner; Kobbe, Jonathan. - : Association for Computational Linguistics, ACL, 2020
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9
Knowledge graphs meet moral values
Hulpus, Ioana; Kobbe, Jonathan; Stuckenschmidt, Heiner; Hirst, Graeme. - : Association for Computational Linguistics, 2020
Abstract: Operationalizing morality is crucial for understanding multiple aspects of society that have moral values at their core {--} such as riots, mobilizing movements, public debates, etc. Moral Foundations Theory (MFT) has become one of the most adopted theories of morality partly due to its accompanying lexicon, the Moral Foundation Dictionary (MFD), which offers a base for computationally dealing with morality. In this work, we exploit the MFD in a novel direction by investigating how well moral values are captured by KGs. We explore three widely used KGs, and provide concept-level analogues for the MFD. Furthermore, we propose several Personalized PageRank variations in order to score all the concepts and entities in the KGs with respect to their relevance to the different moral values. Our promising results help to progress the operationalization of morality in both NLP and KG communities.
Keyword: 004 Informatik
URL: https://madoc.bib.uni-mannheim.de/57763/1/2020.starsem-1.8.pdf
https://madoc.bib.uni-mannheim.de/57763
https://madoc.bib.uni-mannheim.de/57763/
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10
A spreading activation framework for tracking conceptual complexity of texts
Hulpus, Ioana [Verfasser]; Štajner, Sanja [Verfasser]; Stuckenschmidt, Heiner [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2019
DNB Subject Category Language
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11
Exploiting Background Knowledge for Argumentative Relation Classification
Kobbe, Jonathan; Frank, Anette; Opitz, Juri. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2019. : OASIcs - OpenAccess Series in Informatics. 2nd Conference on Language, Data and Knowledge (LDK 2019), 2019
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12
Exploiting Background Knowledge for Argumentative Relation Classification ...
Kobbe, Jonathan; Opitz, Juri; Becker, Maria. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany, 2019
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13
Exploiting background knowledge for argumentative relation classification
Kobbe, Jonathan; Opitz, Juri; Becker, Maria. - : Leibniz-Zentrum für Informatik, 2019
BASE
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14
A spreading activation framework for tracking conceptual complexity of texts
Hulpus, Ioana; Štajner, Sanja; Stuckenschmidt, Heiner. - : Association for Computational Linguistics, ACL, 2019
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15
Word embeddings-based uncertainty detection in financial disclosures
Stuckenschmidt, Heiner; Theil, Christoph Kilian; Štajner, Sanja. - : Association for Computational Linguistics, 2018
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16
Automatic detection of uncertain statements in the financial domain
Theil, Christoph Kilian; Štajner, Sanja; Stuckenschmidt, Heiner. - : Springer International Publishing, 2018
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17
Sentence alignment methods for improving text simplification systems
Rosso, Paolo; Štajner, Sanja; Franco-Salvador, Mark. - : Association for Computational Linguistics, 2017
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18
Automatic assessment of absolute sentence complexity
Štajner, Sanja; Ponzetto, Simone Paolo; Stuckenschmidt, Heiner. - : International Joint Conferences on Artificial Intelligence, 2017
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19
Automatic detection of speculation in policy statements
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20
Cross-Evaluation of entity linking and disambiguation systems for clinical text annotation
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