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: We employ two sources of e-book annotation data: (i) editor tags, and (ii) Amazon search terms. For editor tags, we collect data of 48,705 e-books from 13 publishers, namely Kunstmann, Delius-Klasnig, VUR, HJR, Diogenes, Campus, Kiwi, Beltz, Chbeck, Rowohlt, Droemer, Fischer and Neopubli.  For the Amazon search terms, we collect search query logs of 21,243 e-books for 12 months (i.e., November 2017 to October 2018).  
 Question: what dataset was used?

A: 48,705 e-books from 13 publishers search query logs of 21,243 e-books for 12 months
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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
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Q: Controls are calculated heuristically. All words found in the control word lists are then removed from the reference sentence. The remaining words, which represent the content, are used as input into the model, along with their POS tags and lemmas.

In this way we encourage models to construct a sentence using content and style independently. 
 Question: How they know what are content words?

A:
 words found in the control word lists are then removed The remaining words, which represent the content
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