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.

Example Input: To further study the information encoded in the discourse embeddings, we perform t-SNE clustering BIBREF20 on them, using the best performing model CNN2-DE (global). 
 Question: How are discourse embeddings analyzed?
Example Output: Abstractive

Example Input: In this approach the similarity between two words is not strictly based on their co-occurrence frequencies, but rather on the frequencies of the other words which occur with both of them (i.e., second order co-occurrences). This approach has been shown to be successful in quantifying semantic relatedness BIBREF12 , BIBREF13 . 
 Question: What is a second order co-ocurrence matrix?
Example Output: Extractive

Example Input: For experiments, we train and evaluate our models in the civil law system of mainland China. We collect and construct a large-scale real-world data set of INLINEFORM0 case documents that the Supreme People's Court of People's Republic of China has made publicly available 
 Question: what is the size of the real-world civil case dataset?
Example Output:
Abstractive