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: 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?
Example Output: From Twitter profile descriptions of the users.

Example Input:  For the embeddings, we relied on $AraVec$ BIBREF30 for Arabic, FastText BIBREF31 for French, and Word2vec Google News BIBREF32 for English .  
 Question: What monolingual word representations are used?
Example Output: AraVec for Arabic, FastText for French, and Word2vec Google News for English.

Example Input: They can answer each question with either `yes', `rather yes', `rather no', or `no'. They can supplement each answer with a comment of at most 500 characters. 
 Question: What annotations are present in dataset?
Example Output:
answer each question with either `yes', `rather yes', `rather no', or `no'. can supplement each answer with a comment of at most 500 characters