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: Suppose there are INLINEFORM0 mentions in documents on average, among these global models, NCEL not surprisingly has the lowest time complexity INLINEFORM1 since it only considers adjacent mentions, where INLINEFORM2 is the number of sub-GCN layers indicating the iterations until convergence.
Question: Do they only use adjacent entity mentions or use more than that in some cases (next to adjacent)?

A: NCEL considers only adjacent mentions.
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Q: This section introduces our probabilistic model that infers keyword expectation and trains the target model simultaneously.
Question: What type of classifiers are used?

A: probabilistic model
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Q: Second is our first random seed selected for the classifier which produces a 0.8083 result. While better than the NBSVM solution, we pick the best validation F1 from the 20 seeds we tried. This produced our final submission of 0.8099. Our best model achieved an five-fold average F1 of 0.8254 on the validation set shown in Figure FIGREF27 but a test set F1 of 0.8099 - a drop of 0.0155 in F1 for the true out-of-sample data.
Question: What were their results on the classification and regression tasks

A:
F1 of 0.8099
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