Papers by Heiner Stuckenschmidt
Knowledge Graphs meet Moral Values (2020.starsem-1)
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| Challenge: | Moral Foundations Theory (MFT) is one of the most adopted theories of morality due to its accompanying lexicon, the Moral Foundation Dictionary (MFD). |
| Approach: | They propose to use the Moral Foundation Dictionary to analyze moral values in three widely used KGs and propose several Personalized PageRank variations to score concepts and entities in the KG with respect to their relevance to the different moral values. |
| Outcome: | The proposed methods help to operationalize morality in both NLP and KG communities. |
Unsupervised stance detection for arguments from consequences (2020.emnlp-main)
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| Challenge: | Social media platforms are becoming an essential venue for online deliberation . stance detection is a task to determine whether a text is in favor of, against, or unrelated to a given topic. |
| Approach: | They propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. |
| Outcome: | The proposed method outperforms BERT and can be comparable to other methods. |
Come hither or go away? Recognising pre-electoral coalition signals in the news (2021.emnlp-main)
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Ines Rehbein, Simone Paolo Ponzetto, Anna Adendorf, Oke Bahnsen, Lukas Stoetzer, Heiner Stuckenschmidt
| Challenge: | In this paper, we decompose the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a coalition into two related, but distinct tasks. |
| Approach: | They propose a task of recognizing from news coverage the (un)willingness of political parties to form a coalition from text and a sub-task of predicting the polarity of the signal. |
| Outcome: | The proposed approach improves over a strong monolingual transfer learning baseline. |
A Spreading Activation Framework for Tracking Conceptual Complexity of Texts (P19-1)
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| Challenge: | Existing models for assessing conceptual complexity of texts are lacking . conceptual complexity accounts for background knowledge necessary to understand mentioned concepts . |
| Approach: | They propose an unsupervised approach for assessing conceptual complexity of texts based on spreading activation using DBpedia knowledge graph as a proxy to long-term memory. |
| Outcome: | The proposed model outperforms current state of the art in assessing conceptual complexity of texts. |