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.

[EX Q]: We collected tweets related to five different DDoS attacks on three different American banks. For each attack, all the tweets containing the bank's name posted from one week before the attack until the attack day were collected. There are in total 35214 tweets in the dataset. Only the tweets from the Bank of America attack on 09/19/2012 were used in this experiment. In this subsection we evaluate how good the model generalizes. To achieve that, the dataset is divided into two groups, one is about the attacks on Bank of America and the other group is about PNC and Wells Fargo. The only difference between this experiment and the experiment in section 4.4 is the dataset. In this experiment setting $D_a$ contains only the tweets collected on the days of attack on PNC and Wells Fargo. $D_b$ only contains the tweets collected before the Bank of America attack. 
 Question: What is the training and test data used?
[EX A]: Tweets related to a Bank of America DDos attack were used as training data. The test datasets contain tweets related to attacks to Bank of America, PNC and Wells Fargo.

[EX Q]: Maximum Entropy theory is applied to solve Vietnamese word segmentation BIBREF15 , BIBREF22 , BIBREF23 . There are several studies about the Vietnamese word segmentation task over the last decade. Dinh et al. started this task with Weighted Finite State Transducer (WFST) approach and Neural Network approach BIBREF9 . In addition, machine learning approaches are studied and widely applied to natural language processing and word segmentation as well. In fact, several studies used support vector machines (SVM) and conditional random fields (CRF) for the word segmentation task BIBREF7 , BIBREF8 . 
 Question: Which approaches have been applied to solve word segmentation in Vietnamese?
[EX A]: Maximum Entropy Weighted Finite State Transducer (WFST)  support vector machines (SVM) conditional random fields (CRF)

[EX Q]: While in this paper, we focus on the contexts and names of entities, there is a textual source of information about entities in KBs which we can also make use of: descriptions of entities. We extract Wikipedia descriptions of FIGMENT entities filtering out the entities ( $\sim $ 40,000 out of $\sim $ 200,000) without description. 
 Question: How do you find the entity descriptions?
[EX A]:
Wikipedia