@inproceedings{schoene-etal-2019-dilated,
title = "Dilated {LSTM} with attention for Classification of Suicide Notes",
author = "Schoene, Annika M and
Lacey, George and
Turner, Alexander P and
Dethlefs, Nina",
editor = "Holderness, Eben and
Jimeno Yepes, Antonio and
Lavelli, Alberto and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6217",
doi = "10.18653/v1/D19-6217",
pages = "136--145",
abstract = "In this paper we present a dilated LSTM with attention mechanism for document-level classification of suicide notes, last statements and depressed notes. We achieve an accuracy of 87.34{\%} compared to competitive baselines of 80.35{\%} (Logistic Model Tree) and 82.27{\%} (Bi-directional LSTM with Attention). Furthermore, we provide an analysis of both the grammatical and thematic content of suicide notes, last statements and depressed notes. We find that the use of personal pronouns, cognitive processes and references to loved ones are most important. Finally, we show through visualisations of attention weights that the Dilated LSTM with attention is able to identify the same distinguishing features across documents as the linguistic analysis.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="schoene-etal-2019-dilated">
<titleInfo>
<title>Dilated LSTM with attention for Classification of Suicide Notes</title>
</titleInfo>
<name type="personal">
<namePart type="given">Annika</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Schoene</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">George</namePart>
<namePart type="family">Lacey</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexander</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Turner</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nina</namePart>
<namePart type="family">Dethlefs</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2019-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Eben</namePart>
<namePart type="family">Holderness</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Antonio</namePart>
<namePart type="family">Jimeno Yepes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alberto</namePart>
<namePart type="family">Lavelli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anne-Lyse</namePart>
<namePart type="family">Minard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">James</namePart>
<namePart type="family">Pustejovsky</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fabio</namePart>
<namePart type="family">Rinaldi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Hong Kong</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper we present a dilated LSTM with attention mechanism for document-level classification of suicide notes, last statements and depressed notes. We achieve an accuracy of 87.34% compared to competitive baselines of 80.35% (Logistic Model Tree) and 82.27% (Bi-directional LSTM with Attention). Furthermore, we provide an analysis of both the grammatical and thematic content of suicide notes, last statements and depressed notes. We find that the use of personal pronouns, cognitive processes and references to loved ones are most important. Finally, we show through visualisations of attention weights that the Dilated LSTM with attention is able to identify the same distinguishing features across documents as the linguistic analysis.</abstract>
<identifier type="citekey">schoene-etal-2019-dilated</identifier>
<identifier type="doi">10.18653/v1/D19-6217</identifier>
<location>
<url>https://aclanthology.org/D19-6217</url>
</location>
<part>
<date>2019-11</date>
<extent unit="page">
<start>136</start>
<end>145</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Dilated LSTM with attention for Classification of Suicide Notes
%A Schoene, Annika M.
%A Lacey, George
%A Turner, Alexander P.
%A Dethlefs, Nina
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F schoene-etal-2019-dilated
%X In this paper we present a dilated LSTM with attention mechanism for document-level classification of suicide notes, last statements and depressed notes. We achieve an accuracy of 87.34% compared to competitive baselines of 80.35% (Logistic Model Tree) and 82.27% (Bi-directional LSTM with Attention). Furthermore, we provide an analysis of both the grammatical and thematic content of suicide notes, last statements and depressed notes. We find that the use of personal pronouns, cognitive processes and references to loved ones are most important. Finally, we show through visualisations of attention weights that the Dilated LSTM with attention is able to identify the same distinguishing features across documents as the linguistic analysis.
%R 10.18653/v1/D19-6217
%U https://aclanthology.org/D19-6217
%U https://doi.org/10.18653/v1/D19-6217
%P 136-145
Markdown (Informal)
[Dilated LSTM with attention for Classification of Suicide Notes](https://aclanthology.org/D19-6217) (Schoene et al., Louhi 2019)
ACL
- Annika M Schoene, George Lacey, Alexander P Turner, and Nina Dethlefs. 2019. Dilated LSTM with attention for Classification of Suicide Notes. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), pages 136–145, Hong Kong. Association for Computational Linguistics.