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Come hither or go away? Recognising pre-electoral coalition signals in the news ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.615/ Abstract: In this paper, we introduce the task of political coalition signal prediction from text, that is, the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a government coalition. We decompose our problem into two related, but distinct tasks: (i) predicting whether a reported statement from a politician or a journalist refers to a potential coalition and (ii) predicting the polarity of the signal – namely, whether the speaker is in favour of or against the coalition. For this, we explore the benefits of multi-task learning and investigate which setup and task formulation is best suited for each sub-task. We evaluate our approach, based on hand-coded newspaper articles, covering elections in three countries (Ireland, Germany, Austria) and two languages (English, German). Our results show that the multi-task learning approach can further improve results over a strong ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://underline.io/lecture/37736-come-hither-or-go-awayquestion-recognising-pre-electoral-coalition-signals-in-the-news https://dx.doi.org/10.48448/cdyh-ke40
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Come hither or go away? Recognising pre-electoral coalition signals in the news
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Unsupervised stance detection for arguments from consequences
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Exploiting Background Knowledge for Argumentative Relation Classification
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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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Exploiting Background Knowledge for Argumentative Relation Classification ...
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Exploiting background knowledge for argumentative relation classification
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A spreading activation framework for tracking conceptual complexity of texts
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Word embeddings-based uncertainty detection in financial disclosures
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Automatic detection of uncertain statements in the financial domain
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Sentence alignment methods for improving text simplification systems
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Cross-Evaluation of entity linking and disambiguation systems for clinical text annotation
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