Task: Question Answering
Input (English): We now consider the following problem: given two BabelNet categories $A$ and $B$, predict whether they are likely to be conceptual neighbors based on the sentences from a text corpus in which they are both mentioned. To train such a classifier, we use the distant supervision labels from Section SECREF8 as training data. Once this classifier has been trained, we can then use it to predict conceptual neighborhood for categories for which only few instances are known. To find sentences in which both $A$ and $B$ are mentioned, we rely on a disambiguated text corpus in which mentions of BabelNet categories are explicitly tagged. Such a disambiguated corpus can be automatically constructed, using methods such as the one proposed by BIBREF30 mancini-etal-2017-embedding, for instance. For each pair of candidate categories, we thus retrieve all sentences where they co-occur. Next, we represent each extracted sentence as a vector.  
 Question: How they indentify conceptual neighbours?
Output (English): Once this classifier has been trained, we can then use it to predict conceptual neighborhood for categories for which only few instances are known.