@inproceedings{liu-etal-2019-uncover,
title = "Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization",
author = "Liu, Yingchi and
Li, Quanzhi and
Cifor, Marika and
Liu, Xiaozhong and
Zhang, Qiong and
Si, Luo",
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/D19-1237",
doi = "10.18653/v1/D19-1237",
pages = "2328--2337",
abstract = "The number of personal stories about sexual harassment shared online has increased exponentially in recent years. This is in part inspired by the {\#}MeToo and {\#}TimesUp movements. Safecity is an online forum for people who experienced or witnessed sexual harassment to share their personal experiences. It has collected {\textgreater}10,000 stories so far. Sexual harassment occurred in a variety of situations, and categorization of the stories and extraction of their key elements will provide great help for the related parties to understand and address sexual harassment. In this study, we manually annotated those stories with labels in the dimensions of location, time, and harassers{'} characteristics, and marked the key elements related to these dimensions. Furthermore, we applied natural language processing technologies with joint learning schemes to automatically categorize these stories in those dimensions and extract key elements at the same time. We also uncovered significant patterns from the categorized sexual harassment stories. We believe our annotated data set, proposed algorithms, and analysis will help people who have been harassed, authorities, researchers and other related parties in various ways, such as automatically filling reports, enlightening the public in order to prevent future harassment, and enabling more effective, faster action to be taken.",
}
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<abstract>The number of personal stories about sexual harassment shared online has increased exponentially in recent years. This is in part inspired by the #MeToo and #TimesUp movements. Safecity is an online forum for people who experienced or witnessed sexual harassment to share their personal experiences. It has collected \textgreater10,000 stories so far. Sexual harassment occurred in a variety of situations, and categorization of the stories and extraction of their key elements will provide great help for the related parties to understand and address sexual harassment. In this study, we manually annotated those stories with labels in the dimensions of location, time, and harassers’ characteristics, and marked the key elements related to these dimensions. Furthermore, we applied natural language processing technologies with joint learning schemes to automatically categorize these stories in those dimensions and extract key elements at the same time. We also uncovered significant patterns from the categorized sexual harassment stories. We believe our annotated data set, proposed algorithms, and analysis will help people who have been harassed, authorities, researchers and other related parties in various ways, such as automatically filling reports, enlightening the public in order to prevent future harassment, and enabling more effective, faster action to be taken.</abstract>
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%0 Conference Proceedings
%T Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization
%A Liu, Yingchi
%A Li, Quanzhi
%A Cifor, Marika
%A Liu, Xiaozhong
%A Zhang, Qiong
%A Si, Luo
%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 liu-etal-2019-uncover
%X The number of personal stories about sexual harassment shared online has increased exponentially in recent years. This is in part inspired by the #MeToo and #TimesUp movements. Safecity is an online forum for people who experienced or witnessed sexual harassment to share their personal experiences. It has collected \textgreater10,000 stories so far. Sexual harassment occurred in a variety of situations, and categorization of the stories and extraction of their key elements will provide great help for the related parties to understand and address sexual harassment. In this study, we manually annotated those stories with labels in the dimensions of location, time, and harassers’ characteristics, and marked the key elements related to these dimensions. Furthermore, we applied natural language processing technologies with joint learning schemes to automatically categorize these stories in those dimensions and extract key elements at the same time. We also uncovered significant patterns from the categorized sexual harassment stories. We believe our annotated data set, proposed algorithms, and analysis will help people who have been harassed, authorities, researchers and other related parties in various ways, such as automatically filling reports, enlightening the public in order to prevent future harassment, and enabling more effective, faster action to be taken.
%R 10.18653/v1/D19-1237
%U https://aclanthology.org/D19-1237
%U https://doi.org/10.18653/v1/D19-1237
%P 2328-2337
Markdown (Informal)
[Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization](https://aclanthology.org/D19-1237) (Liu et al., EMNLP-IJCNLP 2019)
ACL