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.

Example Input: Twitter data: We used the Twitter API to scrap tweets with hashtags. All non-English tweets were filtered out by the API. 
 Question: Do they evaluate only on English datasets?
Example Output: Yes

Example Input:  To evaluate real-world performance of our selected classifier (i.e., performance in the absence of model and parameter bias), we perform classification of the holdout set. On this set, our classifier had an accuracy and F1-score of 89.6% and 89.2%, respectively.  
 Question: What was their system's performance?
Example Output: accuracy and F1-score of 89.6% and 89.2%, respectively

Example Input: Gazetteers: 6)Contributor first names; 7)Contributor last names; 8)Contributor types ("soprano", "violinist", etc.); 9)Classical work types ("symphony", "overture", etc.); 10)Musical instruments; 11)Opus forms ("op", "opus"); 12)Work number forms ("no", "number"); 13)Work keys ("C", "D", "E", "F" , "G" , "A", "B", "flat", "sharp"); 14)Work Modes ("major", "minor", "m"). 
 Question: What task-specific features are used?
Example Output:
6)Contributor first names 7)Contributor last names 8)Contributor types ("soprano", "violinist", etc.) 9)Classical work types ("symphony", "overture", etc.) 10)Musical instruments 11)Opus forms ("op", "opus") 12)Work number forms ("no", "number") 13)Work keys ("C", "D", "E", "F" , "G" , "A", "B", "flat", "sharp") 14)Work Modes ("major", "minor", "m")