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: In our experiments, we used TF.IDF-based features over the title and over the content of the article we wanted to classify. We had these features twice – once for the title and once for the the content of the article, as we wanted to have two different representations of the same article. Thus, we used a total of 1,100 TF.IDF-weighted features (800 content + 300 title), limiting the vocabulary to the top 800 and 300 words, respectively (which occurred in more than five articles). 
 Question: what lexical features did they experiment with?

Answer: TF.IDF-based features


Question: Next, for emergent language, we show that the MASC architecture can achieve very high localization accuracy, significantly outperforming the baseline that does not include this mechanism. 
 Question: What evaluation metrics did the authors look at?

Answer: localization accuracy


Question: Overall, this confirms our initial intuition that combining the best performing popularity-based approach with the best similarity-based approach should result in the highest accuracy (i.e., INLINEFORM7 for INLINEFORM8 ). 
 Question: which algorithm was the highest performer?

Answer:
A hybrid model consisting of best performing popularity-based approach with the best similarity-based approach