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.

Ex Input:
Given a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account. 
 Question: Was the approach used in this work to detect fake news fully supervised?

Ex Output:
Yes


Ex Input:
Figure FIGREF23 shows the proposed ontology, which, in our evaluation procedure, was populated with 3121 events entries from 51 documents. 
 Question: How is the effectiveness of this pipeline approach evaluated?

Ex Output:
proposed ontology, which, in our evaluation procedure, was populated with 3121 events entries from 51 documents.


Ex Input:
Most lexical resources for sentiment analysis are in English. To still be able to benefit from these sources, the lexicons in the AffectiveTweets package were translated to Spanish, using the machine translation platform Apertium BIBREF5  
 Question: How was the training data translated?

Ex Output:

using the machine translation platform Apertium