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.

[Q]: Although MCDN doesn't obtain the highest precision, it increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$. 
 Question: What performance did proposed method achieve, how much better is than previous state-of-the-art?
[A]: increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$


[Q]: The results indicate that most performance gains come from words embeddings, style, and morality features. Other features (emotion and sentiment) show lower importance: nevertheless, they still improve the overall system performance (on average 0.35% Macro-F$_1$ improvement) 
 Question: Based on this paper, what is the more predictive set of features to detect fake news?
[A]: words embeddings, style, and morality features


[Q]: Recently, MADAMIRA BIBREF6 system has been evaluated using a blind testset (25K words for Modern Standard Arabic (MSA) selected from Penn Arabic Tree bank (PATB)), and the reported accuracy was 96.2% as the percentage of words where the chosen analysis (provided by SAMA morphological analyzer BIBREF7 ) has the correct lemma. As MSA is usually written without diacritics and IR systems normally remove all diacritics from search queries and indexed data as a basic preprocessing step, so another column for undiacritized lemma is added and it's used for evaluating our lemmatizer and comparing with state-of-the-art system for lemmatization; MADAMIRA. 
 Question: What is the state of the art?
[A]:
 MADAMIRA BIBREF6 system