@inproceedings{bianchi-etal-2021-sweat,
title = "{SWEAT}: Scoring Polarization of Topics across Different Corpora",
author = "Bianchi, Federico and
Marelli, Marco and
Nicoli, Paolo and
Palmonari, Matteo",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.788",
doi = "10.18653/v1/2021.emnlp-main.788",
pages = "10065--10072",
abstract = "Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the Sliced Word Embedding Association Test (SWEAT), a novel statistical measure to compute the relative polarization of a topical wordset across two distributional representations. To this end, SWEAT uses two additional wordsets, deemed to have opposite valence, to represent two different poles. We validate our approach and illustrate a case study to show the usefulness of the introduced measure.",
}
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<abstract>Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the Sliced Word Embedding Association Test (SWEAT), a novel statistical measure to compute the relative polarization of a topical wordset across two distributional representations. To this end, SWEAT uses two additional wordsets, deemed to have opposite valence, to represent two different poles. We validate our approach and illustrate a case study to show the usefulness of the introduced measure.</abstract>
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%0 Conference Proceedings
%T SWEAT: Scoring Polarization of Topics across Different Corpora
%A Bianchi, Federico
%A Marelli, Marco
%A Nicoli, Paolo
%A Palmonari, Matteo
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F bianchi-etal-2021-sweat
%X Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the Sliced Word Embedding Association Test (SWEAT), a novel statistical measure to compute the relative polarization of a topical wordset across two distributional representations. To this end, SWEAT uses two additional wordsets, deemed to have opposite valence, to represent two different poles. We validate our approach and illustrate a case study to show the usefulness of the introduced measure.
%R 10.18653/v1/2021.emnlp-main.788
%U https://aclanthology.org/2021.emnlp-main.788
%U https://doi.org/10.18653/v1/2021.emnlp-main.788
%P 10065-10072
Markdown (Informal)
[SWEAT: Scoring Polarization of Topics across Different Corpora](https://aclanthology.org/2021.emnlp-main.788) (Bianchi et al., EMNLP 2021)
ACL
- Federico Bianchi, Marco Marelli, Paolo Nicoli, and Matteo Palmonari. 2021. SWEAT: Scoring Polarization of Topics across Different Corpora. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10065–10072, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.