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: SVMRank is a modification to SVM that assigns scores to each data point and allows the results to be ranked ( BIBREF26 ). We use SVMRank in the experiments below.  
 Question: what is the supervised model they developed?

SOLUTION: SVMRank

PROBLEM: They annotated 7.8K verbs, reporting an average of 2.4 QA pairs per predicate. Even though multiple annotators were shown to produce greater coverage, their released dataset was produced using only a single annotator per verb. 
 Question: How was coverage measured?

SOLUTION: QA pairs per predicate

PROBLEM: Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts. 
 Question: What data is the language model pretrained on?

SOLUTION:
Chinese general corpus