TASK DEFINITION: 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.
PROBLEM: To assess the consistency of annotations and also eliminate coincidental annotations, we used agreement rates, which is calculated by dividing the number of senses under each category where the annotators annotate consistently by the total number of each kind of sense. And considering the potential impact of unbalanced distribution of senses, we also used the Kappa value.
Question: Which inter-annotator metric do they use?

SOLUTION: agreement rates Kappa value

PROBLEM: As fig:fit shows, estimated test accuracy is highly correlated with actual test accuracy for various datasets, with worst-case values $\mu <1\%$ and $\sigma <5\%$ . Note that the number of free parameters is small ($||\le 6$) compared to the number of points (4249 model-data configurations), demonstrating the appropriateness of the proposed function for modeling the complex error landscape.
Question: What is proof that proposed functional form approximates well generalization error in practice?

SOLUTION: estimated test accuracy is highly correlated with actual test accuracy for various datasets appropriateness of the proposed function for modeling the complex error landscape

PROBLEM: The retweeting behavior of MEPs is captured by their retweet network. Each MEP active on Twitter is a node in this network. An edge in the network between two MEPs exists when one MEP retweeted the other. The weight of the edge is the number of retweets between the two MEPs We measure the cohesion of a political group INLINEFORM0 as the average retweets, i.e., the ratio of the number of retweets between the MEPs in the group INLINEFORM1 to the number of MEPs in the group INLINEFORM2
Question: Do they authors account for differences in usage of Twitter amongst MPs into their model?

SOLUTION:
No