In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Q: We employ a huge dataset of 45,000 self-reported depressed users introduced in BIBREF46 where a lexicon of depression symptoms consisting of 1500 depression-indicative terms was created with the help of psychologist clinician and employed for collecting self-declared depressed individual's profiles. A subset of 8,770 users (24 million time-stamped tweets) containing 3981 depressed and 4789 control users (that do not show any depressive behavior) were verified by two human judges BIBREF46 .

A: How is the data annotated?
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Q: The attention function BIBREF11 is used to compute the similarity score between passages and questions as: INLINEFORM2

A: Do they use attention?
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Q: As a further application of our work, we have carried out a supervised classification task aimed at predicting the degree of harm of an incident directly from the text and the hand-coded features (e.g., external category, medical specialty, location). We also checked if using our unsupervised content-driven cluster labels as additional features can improve the performance of the supervised classification.

A:
How are content clusters used to improve the prediction of incident severity?
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