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:
The annotator carried out all annotation.
Question: How many annotators tagged each tweet?

Ex Output:
One

Ex Input:
To discover topics from the collected tweets, we used a topic modeling approach that fuzzy clusters the semantically related words such as assigning "diabetes", "cancer", and "influenza" into a topic that has an overall "disease" theme BIBREF44 , BIBREF45 . Among topic models, Latent Dirichlet Allocation (LDA) BIBREF49 is the most popular effective model BIBREF50 , BIBREF19 as studies have shown that LDA is an effective computational linguistics model for discovering topics in a corpus BIBREF51 , BIBREF52 . We used the Mallet implementation of LDA BIBREF49 , BIBREF56 with its default settings to explore opinions in the tweets.
Question: How were topics of interest about DDEO identified?

Ex Output:
using topic modeling model Latent Dirichlet Allocation (LDA)

Ex Input:
The Logoscope retrieves newspaper articles from several RSS feeds in French on a daily basis.
Question: How often are the newspaper websites crawled daily?

Ex Output:
RSS feeds in French on a daily basis