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.

Input: Consider Input: 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 .

Output: How is the data annotated?

Input: Consider Input: Secondly, a well-known problem of uneven yet unknown distribution of word senses is alleviated by modifying a naive Bayesian classifier. Thanks to this correction, the classifier is no longer biased towards senses that have more training data. Finally, the aggregated feature values corresponding to target word senses are used to build a naive Bayesian classifier adjusted to a situation of unknown a priori probabilities.

Output: How do they deal with unknown distribution senses?

Input: Consider Input: Our experiments are performed on an actual endangered language, Mboshi (Bantu C25), a language spoken in Congo-Brazzaville, using the bilingual French-Mboshi 5K corpus of BIBREF17. On the Mboshi side, we consider alphabetic representation with no tonal information. On the French side,we simply consider the default segmentation into words.
Output: What is the dataset used in the paper?