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]: In addition to the majority baseline, we also compare our results with a lexicon-based approach. For experimental results, we report majority baseline for each language where the majority baseline corresponds to a model's accuracy if it always predicts the majority class in the dataset. 
 Question: what are the baselines?
[A]: majority baseline lexicon-based approach


[Q]: We sparsify this graph to the union of a minimum spanning tree and a k-Nearest Neighbors (MST-kNN) graph BIBREF14, a geometric construction that removes less important similarities but preserves global connectivity for the graph and, hence, for the dataset. The MST-kNN graph is then analysed with Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18, a multi-resolution graph partitioning method that identifies relevant subgraphs (i.e., clusters of documents) at different levels of granularity. 
 Question: What cluster identification method is used in this paper?
[A]: A combination of Minimum spanning trees, K-Nearest Neighbors and Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18


[Q]: 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?
[A]:
Yes