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.

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?
48,705 e-books from 13 publishers search query logs of 21,243 e-books for 12 months

The corpus is freely available at the following link 
 Question: Is this dataset publicly available?
Yes

First, we compare its performance with other BERT-models and state-of-the-art systems in sentiment analysis, to show its performance for classification tasks.  Second, we compare its performance in a recent Dutch language task, namely the disambiguation of demonstrative pronouns, which allows us to additionally compare the zero-shot performance of our and other BERT models, i.e. using only the pre-trained model without any fine-tuning. 
 Question: What language tasks did they experiment on?

sentiment analysis the disambiguation of demonstrative pronouns,