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.

Q: Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately. The simplest way to generate text embeddings based on term embeddings is to use some kind of aggregation of term embeddings such as an average. This approach was tested for example by BIBREF21 and BIBREF13 . BIBREF22 computed a weighted mean of term embeddings by the construction of a loss function and training weights by the gradient descent method.

Final embeddings for visits are obtained by concatenation of average embeddings calculated separately for the interview and for the medical examination, see Figure FIGREF4 . 
 Question: Which word embeddings do they use to represent medical visits?

A: GloVe concatenation of average embeddings calculated separately for the interview and for the medical examination
****
Q: 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? 

A: Users' tweets
****
Q: Baselines. As none of the existing KBC methods can solve the OKBC problem, we choose various versions of LiLi as baselines.

Single: Version of LiLi where we train a single prediction model INLINEFORM0 for all test relations.

Sep: We do not transfer (past learned) weights for initializing INLINEFORM0 , i.e., we disable LL.

F-th): Here, we use a fixed prediction threshold 0.5 instead of relation-specific threshold INLINEFORM0 .

BG: The missing or connecting links (when the user does not respond) are filled with “@-RelatedTo-@" blindly, no guessing mechanism.

w/o PTS: LiLi does not ask for additional clues via past task selection for skillset improvement. 
 Question: What baseline is used in the experiments?

A:
versions of LiLi
****