In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

The median age is 17 for depressed class versus 19 for control class suggesting either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age for connecting to their peers (social homophily.) Our findings are consistent with the medical literature BIBREF10 as according to BIBREF52 more women than men were given a diagnosis of depression.
Question: What insights into the relationship between demographics and mental health are provided?
either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age more women than men were given a diagnosis of depression

Suppose there are INLINEFORM0 mentions in documents on average, among these global models, NCEL not surprisingly has the lowest time complexity INLINEFORM1 since it only considers adjacent mentions, where INLINEFORM2 is the number of sub-GCN layers indicating the iterations until convergence.
Question: Do they only use adjacent entity mentions or use more than that in some cases (next to adjacent)?
NCEL considers only adjacent mentions.

Another approach for generating query and image representations is treating images as a black box. Given the statistics of our dataset (3B query, image pairs with 220M unique queries and 900M unique images), we know that different queries co-occur with the same images. Intuitively, if a query $q_1$ co-occurs with many of the same images as query $q_2$ , then $q_1$ and $q_2$ are likely to be semantically similar, regardless of the visual content of the shared images. Thus, we can use a method that uses only co-occurrence statistics to better understand how well we can capture relationships between queries.
Question: Could you learn such embedding simply from the image annotations and without using visual information?
Yes