  In this paper, we describe TextEnt, a neural network model that learns
distributed representations of entities and documents directly from a knowledge
base (KB). Given a document in a KB consisting of words and entity annotations,
we train our model to predict the entity that the document describes and map
the document and its target entity close to each other in a continuous vector
space. Our model is trained using a large number of documents extracted from
Wikipedia. The performance of the proposed model is evaluated using two tasks,
namely fine-grained entity typing and multiclass text classification. The
results demonstrate that our model achieves state-of-the-art performance on
both tasks. The code and the trained representations are made available online
for further academic research.
