In this paper, we describe TextEnt, a simple neural network model that learns distributed representations of entities and documents from a KB.
given a document in a KB consisting of words and contextual entities (i.e., entities referred from entity annotations in the document), our model predicts the target entity explained by the document (see Figure 1), and maps the document and its target entity close to each other in a continuous vector space.