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 Q]: We describe our rules for WikiSQL here. Our rule for KBQA is simple without using a curated mapping dictionary. The pipeline of rules in SequentialQA is similar to that of WikiSQL. 
 Question: Are the rules dataset specific?
[EX A]: Yes-no

[EX Q]: Table TABREF15 shows a comparison of the results on SimCluster versus K-means algorithm. Here our SimCluster algorithm improves the F1-scores from 0.412 and 0.417 in the two domains to 0.442 and 0.441. The ARI scores also improve from 0.176 and 0.180 to 0.203 and 0.204. 
 Question: Do they use the same distance metric for both the SimCluster and K-means algorithm?
[EX A]: Yes-no

[EX Q]: We use the existing large-scale antonym and synonym pairs previously used by Nguyen:16. Originally, the data pairs were collected from WordNet BIBREF9 and Wordnik. 
 Question: What dataset do they use to evaluate their method?
[EX A]:
Extractive