In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency. We collect all binomials but make predictions only on binomials appearing at least 30 times total, stratified by subreddit. However, none of these features appear to be particularly predictive across the board (Table TABREF15). A simple linear regression model predicts close to random, which bolsters the evidence that these classical rules for frozen binomials are not predictive for general binomials.
How was performance of previously proposed rules at very large scale?

In the sentiment analysis experiments, we found that a tweet may contain multiple sentiments. The groundtruth labels contain 210 positive sentiments, 521 neutral sentiments, and 305 negative sentiments to the candidates.
Is datasets for sentiment analysis balanced?

To investigate the extent to which the text obtained from the two platforms of Yahoo! Answers and Twitter reflect the true attributes of neighbourhoods, we first study whether there are significant, strong and meaningful correlations between the terms present in each corpus and the many neighbourhood attributes through the Pearson correlation coefficient $
ho $ .
What do the correlation demonstrate?