In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

Ex Input:
Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API. Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present. 
 Question: What is the source of the "control" corpus?

Ex Output:
Abstractive


Ex Input:
The GENIA Corpus BIBREF3 contains biomedical abstracts from the PubMed database. We use GENIA technical term annotations 3.02, which cover linguistic expressions to entities of interest in molecular biology, e.g. proteins, genes and cells. CoNLL2003 BIBREF14 is a standard NER dataset based on the Reuters RCV-1 news corpus. It covers named entities of type person, location, organization and misc.

For testing the overall annotation performance, we utilize CoNLL2003-testA and a 50 document split from GENIA.  
 Question: what standard dataset were used?

Ex Output:
Extractive


Ex Input:
Active learning sharply increases the performance of iteratively trained machine learning models by selectively determining which unlabeled samples should be annotated.  
 Question: What is active learning?

Ex Output:

Abstractive