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Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
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In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
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Imputing out-of-vocabulary embeddings with LOVE makes language models robust with little cost
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In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
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Verwendung von Wissensgraphen zur inhaltlichen Ergänzung kleinerer Textkorpora ...
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Verwendung von Wissensgraphen zur inhaltlichen Ergänzung kleinerer Textkorpora ...
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Semantische Suche mit Word Embeddings für ein mehrsprachiges Wörterbuchportal ...
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Semantische Suche mit Word Embeddings für ein mehrsprachiges Wörterbuchportal ...
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Semantische Suche mit Word Embeddings für ein mehrsprachiges Wörterbuchportal ...
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Semantische Suche mit Word Embeddings für ein mehrsprachiges Wörterbuchportal ...
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Emotion and Reason in Political Language ...
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Abstract:
This paper studies the use of emotion and reason in political discourse. Adopting computational-linguistics techniques to construct a validated text-based scale, we measure emotionality in 6 million speeches given in U.S. Congress over the years 1858-2014. Intuitively, emotionality spikes during times of war and is highest in speeches about patriotism. In the time series, emotionality was relatively low and stable in earlier years but increased significantly starting in the late 1970s. Across Congress Members, emotionality is higher for Democrats, for women, for ethnic/religious minorities, for the opposition party, and for members with ideologically extreme roll-call voting records. ... : The Economic Journal, 132 (643) ...
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Keyword:
Emotions; Political Rhetoric; U.S. Congress; Word Embeddings
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URL: https://dx.doi.org/10.3929/ethz-b-000533392 http://hdl.handle.net/20.500.11850/533392
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Measuring and Comparing Social Bias in Static and Contextual Word Embeddings
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In: Dissertations (2022)
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A Generative Model for Topic Discovery and Polysemy Embeddings on Directed Attributed Networks
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In: Symmetry; Volume 14; Issue 4; Pages: 703 (2022)
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A Method of Short Text Representation Fusion with Weighted Word Embeddings and Extended Topic Information
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In: Sensors; Volume 22; Issue 3; Pages: 1066 (2022)
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Emotion and Reason in Political Language
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In: The Economic Journal, 132 (643) (2022)
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When Classifying Arguments, BERT Doesn't Care About Word Order. Except When It Matters
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Tackling Morphological Analogies Using Deep Learning -- Extended Version
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In: https://hal.inria.fr/hal-03425776 ; 2021 (2021)
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About Neural Networks and Writing Definitions
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In: ISSN: 2160-5076 ; Dictionaries: Journal of the Dictionary Society of North America Dictionary Society of North America ; https://hal.archives-ouvertes.fr/hal-03547452 ; Dictionaries: Journal of the Dictionary Society of North America Dictionary Society of North America, 2021, 42 (2), ⟨10.1353/dic.2021.0022⟩ (2021)
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Models of diachronic semantic change using word embeddings ; Modèles diachroniques à base de plongements de mot pour l'analyse du changement sémantique
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In: https://tel.archives-ouvertes.fr/tel-03199801 ; Document and Text Processing. Université Paris-Saclay, 2021. English. ⟨NNT : 2021UPASG006⟩ (2021)
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Multilingual word embeddings and low resources: identifying influence in Antiquity
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In: JADH 2021 ; JADH 2021 “Digital Humanities and COVID-19” ; https://hal.archives-ouvertes.fr/hal-03340641 ; JADH 2021 “Digital Humanities and COVID-19”, Organizing Committee, Japanese Association for Digital Humanities, Sep 2021, Tokyo, Japan. pp.51-54 ; https://www.hi.u-tokyo.ac.jp/ (2021)
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Injecting Inductive Biases into Distributed Representations of Text ...
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