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.
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Question: For all tasks, we use a TensorFlow implementation. AraNet predicts age, dialect, gender, emotion, irony, and sentiment from social media posts. It delivers state-of-the-art and competitive performance on these tasks and has the advantage of using a unified, simple framework based on the recently-developed BERT model.  
 Question: Did they experiment on all the tasks?

Answer: Yes


Question: We extract user's age by applying regular expression patterns to profile descriptions (such as "17 years old, self-harm, anxiety, depression") BIBREF41 . We compile "age prefixes" and "age suffixes", and use three age-extraction rules: 1. I am X years old 2. Born in X 3. X years old, where X is a "date" or age (e.g., 1994). We selected a subset of 1464 users INLINEFORM0 from INLINEFORM1 who disclose their gender in their profile description. 
 Question: Where does the information on individual-level demographics come from?

Answer: From Twitter profile descriptions of the users.


Question: We use precision, recall and F-measure to evaluate the detected revisions. 
 Question: What metrics are used to evaluation revision detection?

Answer:
precision recall F-measure