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: As one of our aims is to compare coreference chain properties in automatic translation with those of the source texts and human reference, we derive data from ParCorFull, an English-German corpus annotated with full coreference chains BIBREF46.
Question: What languages are seen in the news and TED datasets?

A: English German
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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: In order to further analysis the embeddings learned by our method, we use t-SNE tool BIBREF27 to visualize the learned embedding.
Question: What further analysis is done?

A:
we use t-SNE tool BIBREF27 to visualize the learned embedding
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