Dilated LSTM with ranked units for Classification of suicide note

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. This work focuses on identifying suicide notes from other types of text in a document-level classification task, using a hierarchical recurrent neural network to uncover linguistic patterns in the data.

Speaker bio: Annika Marie Schoene is a third-year PhD candidate in Natural Language Processing at the University of Hull and is affiliated to IBM Research UK. The main focus of her work lies in investigating recurrent neural networks for fine-grained emotion detection in social media data. She also has an interest in mental health issues on social media, where she looks at how to identify suicidal ideation in textual data.