SMAPH is an environment allowing the exploitation of an entity annotation system specific for web queries and very short text (like questions). SMAPH alleviates the noise and irregularities that characterize the language of queries and puts queries in a larger context in which it is easier to make sense of them. The key algorithmic idea is to first discover a candidate set of entities and then link-back those entities to their mentions occurring in the input query. This allows us to confine the possible concepts pertinent to the query to only the ones really mentioned in it. The link-back is implemented via a collective disambiguation step based upon a supervised ranking model that makes one joint prediction for the annotation of the complete query optimizing directly the F1 measure.
