@inproceedings{kshirsagar-etal-2017-detecting,
title = "Detecting and Explaining Crisis",
author = "Kshirsagar, Rohan and
Morris, Robert and
Bowman, Samuel",
booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
month = aug,
year = "2017",
address = "Vancouver, BC",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-3108",
doi = "10.18653/v1/W17-3108",
pages = "66--73",
abstract = "Individuals on social media may reveal themselves to be in various states of crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis from social media text automatically and accurately can have profound consequences. However, detecting a general state of crisis without explaining why has limited applications. An explanation in this context is a coherent, concise subset of the text that rationalizes the crisis detection. We explore several methods to detect and explain crisis using a combination of neural and non-neural techniques. We evaluate these techniques on a unique data set obtained from Koko, an anonymous emotional support network available through various messaging applications. We annotate a small subset of the samples labeled with crisis with corresponding explanations. Our best technique significantly outperforms the baseline for detection and explanation.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kshirsagar-etal-2017-detecting">
<titleInfo>
<title>Detecting and Explaining Crisis</title>
</titleInfo>
<name type="personal">
<namePart type="given">Rohan</namePart>
<namePart type="family">Kshirsagar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Robert</namePart>
<namePart type="family">Morris</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samuel</namePart>
<namePart type="family">Bowman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-aug</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vancouver, BC</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Individuals on social media may reveal themselves to be in various states of crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis from social media text automatically and accurately can have profound consequences. However, detecting a general state of crisis without explaining why has limited applications. An explanation in this context is a coherent, concise subset of the text that rationalizes the crisis detection. We explore several methods to detect and explain crisis using a combination of neural and non-neural techniques. We evaluate these techniques on a unique data set obtained from Koko, an anonymous emotional support network available through various messaging applications. We annotate a small subset of the samples labeled with crisis with corresponding explanations. Our best technique significantly outperforms the baseline for detection and explanation.</abstract>
<identifier type="citekey">kshirsagar-etal-2017-detecting</identifier>
<identifier type="doi">10.18653/v1/W17-3108</identifier>
<location>
<url>https://aclanthology.org/W17-3108</url>
</location>
<part>
<date>2017-aug</date>
<extent unit="page">
<start>66</start>
<end>73</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Detecting and Explaining Crisis
%A Kshirsagar, Rohan
%A Morris, Robert
%A Bowman, Samuel
%S Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality
%D 2017
%8 aug
%I Association for Computational Linguistics
%C Vancouver, BC
%F kshirsagar-etal-2017-detecting
%X Individuals on social media may reveal themselves to be in various states of crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis from social media text automatically and accurately can have profound consequences. However, detecting a general state of crisis without explaining why has limited applications. An explanation in this context is a coherent, concise subset of the text that rationalizes the crisis detection. We explore several methods to detect and explain crisis using a combination of neural and non-neural techniques. We evaluate these techniques on a unique data set obtained from Koko, an anonymous emotional support network available through various messaging applications. We annotate a small subset of the samples labeled with crisis with corresponding explanations. Our best technique significantly outperforms the baseline for detection and explanation.
%R 10.18653/v1/W17-3108
%U https://aclanthology.org/W17-3108
%U https://doi.org/10.18653/v1/W17-3108
%P 66-73
Markdown (Informal)
[Detecting and Explaining Crisis](https://aclanthology.org/W17-3108) (Kshirsagar et al., 2017)
ACL
- Rohan Kshirsagar, Robert Morris, and Samuel Bowman. 2017. Detecting and Explaining Crisis. In Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality, pages 66–73, Vancouver, BC. Association for Computational Linguistics.