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.
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Question: Compared to the in-context-parsing-based system, the combination with exact string matching yields a gain in recall of over 6%, and the combination with inflectional string matching yields an even bigger gain of almost 8%, at precision losses of 0.6% and 0.8%, respectively. 
 Question: What compleentary PIE extraction methods are used to increase reliability further?

Answer: exact string matching inflectional string matching


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 . 
 Question: Which word embeddings do they use to represent medical visits?

Answer: GloVe concatenation of average embeddings calculated separately for the interview and for the medical examination


Question: The input for both tasks consists of news articles in free-text format, collected from 36 propagandist and 12 non-propagandist news outlets and then annotated by professional annotators.  
 Question: What dataset was used?

Answer:
 news articles in free-text format