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.

 In Section SECREF16, we first provide more details about the experimental setting that we followed.  As explained in Section SECREF3, we used BabelNet BIBREF29 as our reference taxonomy. BabelNet is a large-scale full-fledged taxonomy consisting of heterogeneous sources such as WordNet BIBREF36, Wikidata BIBREF37 and WiBi BIBREF38, making it suitable to test our hypothesis in a general setting.  To test our proposed category induction model, we consider all BabelNet categories with fewer than 50 known instances. This is motivated by the view that conceptual neighborhood is mostly useful in cases where the number of known instances is small. For each of these categories, we split the set of known instances into 90% for training and 10% for testing. To tune the prior probability $\lambda _A$ for these categories, we hold out 10% from the training set as a validation set. 
 Question: What experiments they perform to demonstrate that their approach leads more accurate region based representations?
 To test our proposed category induction model, we consider all BabelNet categories with fewer than 50 known instances. This is motivated by the view that conceptual neighborhood is mostly useful in cases where the number of known instances is small. For each of these categories, we split the set of known instances into 90% for training and 10% for testing.

As a first corpus, we compiled INLINEFORM0 that represents a collection of 80 excerpts from scientific works including papers, dissertations, book chapters and technical reports, which we have chosen from the well-known Digital Bibliography & Library Project (DBLP) platform. As a second corpus, we compiled INLINEFORM0 , which represents a collection of 1,645 chat conversations of 550 sex offenders crawled from the Perverted-Justice portal. As a third corpus, we compiled INLINEFORM0 , which is a collection of 200 aggregated postings crawled from the Reddit platform. 
 Question: What size are the corpora?
80 excerpts from scientific works collection of 1,645 chat conversations collection of 200 aggregated postings

When evaluating classifiers it is common to use accuracy, precision and recall as well as Hamming loss.  It has been shown that when calculating precision and recall on multi-label classifiers, it can be advantageous to use micro averaged precision and recall BIBREF6 . The formulas for micro averaged precision are expressed as DISPLAYFORM0 DISPLAYFORM1 
 Question: what evaluation metrics are discussed?

precision  recall  Hamming loss micro averaged precision and recall 

