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:
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:
We collect three years of online news articles from June 2016 to June 2019. 
 Question: What unlabeled corpus did they use?

Ex Output:
three years of online news articles from June 2016 to June 2019


Ex Input:
UTD aims to automatically identify and cluster repeated terms (e.g. words or phrases) from speech. To circumvent the exhaustive DTW-based search limited by INLINEFORM0 time BIBREF6 , we exploit the scalable UTD framework in the Zero Resource Toolkit (ZRTools) BIBREF7 , which permits search in INLINEFORM1 time. 
 Question: How is the vocabulary of word-like or phoneme-like units automatically discovered?

Ex Output:

Zero Resource Toolkit (ZRTools) BIBREF7