TASK DEFINITION: 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.
PROBLEM: We evaluate the TN topic model quantitatively with standard topic model measures such as test-set perplexity, likelihood convergence and clustering measures. Qualitatively, we evaluate the model by visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task. 
 Question: What are the measures of "performance" used in this paper?

SOLUTION: test-set perplexity, likelihood convergence and clustering measures visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task

PROBLEM: We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement – we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users. 
 Question: What is the source of the textual data? 

SOLUTION: Users' tweets

PROBLEM: The whole dataset consists of 81,826 samples annotated by native English speakers. 80% of them are used as training set. 10% of them are used as validation set while the rest is used as test set. 3000 hard samples are selected from the test set. 
 Question: What is the difference between the full test set and the hard test set?

SOLUTION:
3000 hard samples are selected from the test set