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:
In a recent work on sentiment analysis in Turkish BIBREF10, they learn embeddings using Turkish social media. They use the word2vec algorithm, create several unsupervised hand-crafted features, generate document vectors and feed them as input into the support vector machines (SVM) approach. We outperform this baseline approach using more effective word embeddings and supervised hand-crafted features. 
 Question: What baseline method is used?

Ex Output:
using word2vec to create features that are used as input to the SVM


Ex Input:
Named Entity Recognition (NER) in the Biomedical domain usually includes recognition of entities such as proteins, genes, diseases, treatments, drugs, etc.  
 Question: What is NER?

Ex Output:
Named Entity Recognition


Ex Input:
Also, we compared our models with other existing works on this dataset including OpATT BIBREF6 and Neural Content Planning with conditional copy (NCP+CC) BIBREF4. 
 Question: What is the state-of-the-art model for the task?

Ex Output:

OpATT BIBREF6 Neural Content Planning with conditional copy (NCP+CC) BIBREF4