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.
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Question: While in this paper, we focus on the contexts and names of entities, there is a textual source of information about entities in KBs which we can also make use of: descriptions of entities. We extract Wikipedia descriptions of FIGMENT entities filtering out the entities ( $\sim $ 40,000 out of $\sim $ 200,000) without description.

Answer: How do you find the entity descriptions?

Question: Figure FIGREF23 shows the proposed ontology, which, in our evaluation procedure, was populated with 3121 events entries from 51 documents.

Answer: How is the effectiveness of this pipeline approach evaluated?

Question: We address the robustness problem on top of GE-FL BIBREF0 , a GE method which leverages labeled features as prior knowledge.

Answer:
What background knowledge do they leverage?