instruction:
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.
question:
The real-time tweets scores were calculated in the same way as the historical data and summed up for a minute and sent to the machine learning model with the Bitcoin price in the previous minute and the rolling average price. It predicted the next minute's Bitcoin price from the given data. After the actual price arrived, the RMS value was calculated and the machine learning model updated itself to predict with better understanding the next value. 
 Question: What experimental evaluation is used?
answer:
Abstractive


question:
The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging.  
 Question: Does the paper report the accuracy of the model?
answer:
Yes-no


question:
Only the information concerned with the dictionary definitions are used there, discarding the polarity scores. However, when we utilise the supervised score (+1 or -1), words of opposite polarities (e.g. “happy" and “unhappy") get far away from each other as they are translated across coordinate regions. 
 Question: How are the supervised scores of the words calculated?
answer:

Extractive