Dilated LSTM with attention for Classification of Suicide Notes

WS 2019 Annika M SchoeneGeorge LaceyAlex Turnerer PNina Dethlefs

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)... (read more)

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