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 dataset consists of more than $0.3$ million records and has been made available for future research. 
 Question: What is the size of the dataset?

Ex Output:
$0.3$ million records


Ex Input:
The corpus used for sentiment analysis is the IMDb dataset of movie reviews by BIBREF11 while that for NER is Groningen Meaning Bank (GMB) by BIBREF12, containing 47,959 sentence samples. 
 Question: What sentiment analysis dataset is used?

Ex Output:
IMDb dataset of movie reviews


Ex Input:
We need good amount of data to try deep learning state-of-the-art algorithms. There are lot of open datasets available for names, locations, organisations, but not for topics as defined in Abstract above. Also defining and inferring topics is an individual preference and there are no fix set of rules for its definition. But according to our definition, we can use wikipedia titles as our target topics. English wikipedia dataset has more than 18 million titles if we consider all versions of them till now. We had to clean up the titles to remove junk titles as wikipedia title almost contains all the words we use daily.  After doing some more cleaning we were left with 10 million titles. We have a dump of 15 million English news articles published in past 4 years. Further, we reduced number of articles by removing duplicate and near similar articles. We used our pre-trained doc2vec models and cosine similarity to detect almost similar news articles. 
 Question: How large is the dataset they used?

Ex Output:

English wikipedia dataset has more than 18 million a dump of 15 million English news articles