Dilated LSTM with attention for Classification of Suicide Notes

Annika M Schoene, George Lacey, Alexander P Turner, Nina Dethlefs


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.
Anthology ID:
D19-6217
Volume:
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | Louhi | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
136–145
Language:
URL:
https://www.aclweb.org/anthology/D19-6217
DOI:
10.18653/v1/D19-6217
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-6217.pdf