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We analyze bias in historical corpora as encoded in diachronic distributional semantic models by focusing on two specific forms of bias, namely a political (i.e., anti-communism) and racist (i.e., antisemitism) one.
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2019
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K. Ethayarajh, D. Duvenaud, and G. Hirst, “Understanding undesirable word embedding associations,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019, pp. 1696–1705
2019
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H. Gonen and Y. Goldberg, “Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Volume 1 (Long and Short Papers) , J. Burstein, C. Doran, and T. Solorio, Eds., 2019, pp. 609–614
2019
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2016
Cited alongside, same era.
W. L. Hamilton, J. Leskovec, and D. Jurafsky, “Diachronic word embeddings reveal statistical laws of semantic change,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2016, pp. 1489–1501
2016
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2017
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A. Caliskan, J. J. Bryson, and A. Narayanan, “Semantics derived automatically from language corpora contain human-like biases,” Science , vol. 356, no. 6334, pp. 183–186, 2017
2017
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S. Eger, T. vor der Brück, and A. Mehler, “A comparison of four character-level string-to-string translation models for (OCR) spelling error correction,” The Prague bulletin of mathematical linguistics , vol. 105, no. 1, pp. 77 – 99, 2017
2017
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G. Glavaš, F. Nanni, and S. P. Ponzetto, “Unsupervised cross-lingual scaling of political texts,” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers) , 2017, pp. 688–693
2017
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N. Garg, L. Schiebinger, D. Jurafsky, and J. Zou, “Word embeddings quantify 100 years of gender and ethnic stereotypes,” Proceedings of the National Academy of Sciences of the United States of America , vol. 115, no. 16, pp. E3635–E3644, 2018
2018
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2019
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2019
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2019
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A. Lauscher and G. Glavaš, “Are we consistently biased? multidimensional analysis of biases in distributional word vectors,” in Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019) , 2019, pp. 85–91
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2019
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J. Zhao, T. Wang, M. Yatskar, R. Cotterell, V. Ordonez, and K.-W. Chang, “Gender bias in contextualized word embeddings,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , 2019, pp. 629–634
2019
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U. Jun, Die Parteien nach der Bundestagswahl 2017: Aktuelle Entwicklungen des Parteienwettbewerbs in Deutschland . Springer-Verlag, 2020
2020
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A. Lauscher, G. Glavaš, S. P. Ponzetto, and I. Vulić, “A general framework for implicit and explicit debiasing of distributional word vector spaces,” in Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020 , 2020, pp. 8131–8138
2020
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A. Lauscher, R. Takieddin, S. P. Ponzetto, and G. Glavaš, “AraWEAT: Multidimensional analysis of biases in Arabic word embeddings,” in Proceedings of the Fifth Arabic Natural Language Processing Workshop , 2020, pp. 192–199
2020
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M. Rovera, F. Nanni, and S. P. Ponzetto, “Event-based access to historical italian war memoirs,” Journal on Computing and Cultural Heritage , vol. 14, no. 1, 2021
2021
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