instruction:
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.
question:
We asked medical doctors experienced in extracting knowledge related to medical entities from texts to annotate the entities described above. Initially, we asked four annotators to test our guidelines on two texts. Subsequently, identified issues were discussed and resolved. Following this pilot annotation phase, we asked two different annotators to annotate two case reports according to our guidelines. The same annotators annotated an overall collection of 53 case reports. The annotation was performed using WebAnno BIBREF7, a web-based tool for linguistic annotation. The annotators could choose between a pre-annotated version or a blank version of each text. The pre-annotated versions contained suggested entity spans based on string matches from lists of conditions and findings synonym lists. 
 Question: How was annotation performed?
answer:
Experienced medical doctors used a linguistic annotation tool to annotate entities.


question:
The second algorithm incorporates recent ideas of data selection in machine translation BIBREF7 . We develop a novel re-weighting method to resolve these problems, using ideas inspired by data selection in machine translation BIBREF26 , BIBREF7 . 
 Question: What is the data selection paper in machine translation
answer:
BIBREF7 BIBREF26 


question:
The corpus consists of 53 documents, which contain an average number of 156.1 sentences per document, each with 19.55 tokens on average. 
 Question: How many documents are in the new corpus?
answer:

53 documents