instruction:
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.
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:
Dataset Quality Analysis ::: Inter-Annotator Agreement (IAA)
To estimate dataset consistency across different annotations, we measure F1 using our UA metric with 5 generators per predicate. Dataset Quality Analysis ::: Dataset Assessment and Comparison
We assess both our gold standard set and the recent Dense set against an integrated expert annotated sample of 100 predicates. Dataset Quality Analysis ::: Agreement with PropBank Data
It is illuminating to observe the agreement between QA-SRL and PropBank (CoNLL-2009) annotations BIBREF7.
answer:
How was quality measured?

question:
Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately. The simplest way to generate text embeddings based on term embeddings is to use some kind of aggregation of term embeddings such as an average. This approach was tested for example by BIBREF21 and BIBREF13 . BIBREF22 computed a weighted mean of term embeddings by the construction of a loss function and training weights by the gradient descent method.

Final embeddings for visits are obtained by concatenation of average embeddings calculated separately for the interview and for the medical examination, see Figure FIGREF4 .
answer:
Which word embeddings do they use to represent medical visits?