@inproceedings{chang-mckeown-2019-automatically,
title = "Automatically Inferring Gender Associations from Language",
author = "Chang, Serina and
McKeown, Kathy",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1579",
doi = "10.18653/v1/D19-1579",
pages = "5746--5752",
abstract = "In this paper, we pose the question: do people talk about women and men in different ways? We introduce two datasets and a novel integration of approaches for automatically inferring gender associations from language, discovering coherent word clusters, and labeling the clusters for the semantic concepts they represent. The datasets allow us to compare how people write about women and men in two different settings {--} one set draws from celebrity news and the other from student reviews of computer science professors. We demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. Human evaluations show that our methods significantly outperform strong baselines.",
}
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%0 Conference Proceedings
%T Automatically Inferring Gender Associations from Language
%A Chang, Serina
%A McKeown, Kathy
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F chang-mckeown-2019-automatically
%X In this paper, we pose the question: do people talk about women and men in different ways? We introduce two datasets and a novel integration of approaches for automatically inferring gender associations from language, discovering coherent word clusters, and labeling the clusters for the semantic concepts they represent. The datasets allow us to compare how people write about women and men in two different settings – one set draws from celebrity news and the other from student reviews of computer science professors. We demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. Human evaluations show that our methods significantly outperform strong baselines.
%R 10.18653/v1/D19-1579
%U https://aclanthology.org/D19-1579
%U https://doi.org/10.18653/v1/D19-1579
%P 5746-5752
Markdown (Informal)
[Automatically Inferring Gender Associations from Language](https://aclanthology.org/D19-1579) (Chang & McKeown, EMNLP-IJCNLP 2019)
ACL
- Serina Chang and Kathy McKeown. 2019. Automatically Inferring Gender Associations from Language. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5746–5752, Hong Kong, China. Association for Computational Linguistics.