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]: For example, it can be used to extract features for usage with other machine learning tools, or to evaluate given features with the existing classifiers or regressors. Figure FIGREF19 demonstrates a simple feature extractor that retrieves the sentence length. 
 Question: Do they show an example of usage for INFODENS?
[EX A]: Yes

[EX Q]:  Recurrent Neural Network (RNN) Language Models
are trained to output the probability distribution of the upcoming word given a context, without explicitly representing the structure of the context BIBREF9, BIBREF10. ActionLSTM
models the linearized bracketed tree structure of a sentence by learning to predict the next action required to construct a phrase-structure parse BIBREF16. Generative Recurrent Neural Network Grammars (RNNG)
jointly model the word sequence as well as the underlying syntactic structure BIBREF18. 
 Question: What are the baseline models?
[EX A]: Recurrent Neural Network (RNN) ActionLSTM Generative Recurrent Neural Network Grammars (RNNG)

[EX Q]: For Turkish, as the first dataset, we utilised the movie reviews which are collected from a popular website. The number of reviews in this movie corpus is 20,244 and the average number of words in reviews is 39. Each of these reviews has a star-rating score which is indicative of sentiment. These polarity scores are between the values 0.5 and 5, at intervals of 0.5. We consider a review to be negative it the score is equal to or lower than 2.5. On the other hand, if it is equal to or higher than 4, it is assumed to be positive. We have randomly selected 7,020 negative and 7,020 positive reviews and processed only them.

 
 Question: What details are given about the movie domain dataset?
[EX A]:
there are 20,244 reviews divided into positive and negative with an average 39 words per review, each one having a star-rating score