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1
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02425462 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (3), pp.559-601. ⟨10.1162/coli_a_00357⟩ ; https://www.mitpressjournals.org/doi/abs/10.1162/coli_a_00357 (2019)
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2
Learning Outside the Box: Discourse-level Features Improve Metaphor Identification. ...
Mu, Jesse; Yannakoudakis, Helen; Shutova, Ekaterina. - : Apollo - University of Cambridge Repository, 2019
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3
Abusive Language Detection with Graph Convolutional Networks ...
Mishra, Pushkar; Del Tredici, Marco; Yannakoudakis, Helen. - : Apollo - University of Cambridge Repository, 2019
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4
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings ...
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5
Learning Outside the Box: Discourse-level Features Improve Metaphor Identification ...
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6
Abusive Language Detection with Graph Convolutional Networks ...
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7
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods ...
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8
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
Ponti, Edoardo; O'Horan, Helen; Berzak, Yevgeni. - : Apollo - University of Cambridge Repository, 2019
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9
Author Profiling for Hate Speech Detection ...
Abstract: The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of abusive and offensive language on the Internet. Previous research suggests that such hateful content tends to come from users who share a set of common stereotypes and form communities around them. The current state-of-the-art approaches to hate speech detection are oblivious to user and community information and rely entirely on textual (i.e., lexical and semantic) cues. In this paper, we propose a novel approach to this problem that incorporates community-based profiling features of Twitter users. Experimenting with a dataset of 16k tweets, we show that our methods significantly outperform the current state of the art in hate speech detection. Further, we conduct a qualitative analysis of model characteristics. We release our code, pre-trained models and all the resources used in the public domain. ... : Proceedings of the 27th International Conference on Computational Linguistics (COLING) 2018. arXiv admin note: text overlap with arXiv:1809.00378 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1902.06734
https://arxiv.org/abs/1902.06734
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10
Learning Outside the Box: Discourse-level Features Improve Metaphor Identification.
Mu, Jesse; Yannakoudakis, Helen; Shutova, Ekaterina. - : In Proceedings of the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics., 2019
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11
Abusive Language Detection with Graph Convolutional Networks
Mishra, Pushkar; Del Tredici, Marco; Yannakoudakis, Helen. - : Association for Computational Linguistics, 2019. : Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019
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12
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Reichart, Roi; Shutova, Ekaterina; Korhonen, Anna-Leena. - : MIT Press - Journals, 2019. : COMPUTATIONAL LINGUISTICS, 2019
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13
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
In: Computational Linguistics, Vol 45, Iss 3, Pp 559-601 (2019) (2019)
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