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.

Input: Consider 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?

Output: IMDb dataset of movie reviews


Input: Consider Input: The observed performance of both the BasicDCGAN and MultitaskDCGAN using 3-classes is comparable to the state-of-the-art, with 49.80% compared to 49.99% reported in BIBREF16  
 Question: What model achieves state of the art performance on this task?

Output: BIBREF16


Input: Consider Input: Chowdhury BIBREF14 and Thomas et al. BIBREF11 proposed methods that use linguistic phenomenons and two-stage SVM to classify DDIs. FBK-irst BIBREF10 is a follow-on work which applies kernel method to the existing model and outperforms it. Neural network based approaches have been proposed by several works. Liu et al. BIBREF9 employ CNN for DDI extraction for the first time which outperforms the traditional machine learning based methods.  Sahu et al. BIBREF12 proposed LSTM based DDI extraction approach and outperforms CNN based approach, since LSTM handles sentence as a sequence instead of slide windows. 
 Question: What are the existing methods mentioned in the paper?


Output: Chowdhury BIBREF14 and Thomas et al. BIBREF11 FBK-irst BIBREF10 Liu et al. BIBREF9 Sahu et al. BIBREF12