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.

The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061. 
 Question: Which dimensionality do they use for their embeddings?
1061

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?
Yes

Baseline Experiments
In this section, we briefly describe a baseline and evaluation scripts that we release, with a detailed documentation, along with the corpus. 
 Question: What baseline system is proposed?

Answer with content missing: (Baseline Method section) We implemented a simple approach inspired by previous work on concept map generation and keyphrase extraction.