  This study proposes a Neural Attentive Bag-of-Entities model, which is a
neural network model that performs text classification using entities in a
knowledge base. Entities provide unambiguous and relevant semantic signals that
are beneficial for capturing semantics in texts. We combine simple high-recall
entity detection based on a dictionary, to detect entities in a document, with
a novel neural attention mechanism that enables the model to focus on a small
number of unambiguous and relevant entities. We tested the effectiveness of our
model using two standard text classification datasets (i.e., the 20 Newsgroups
and R8 datasets) and a popular factoid question answering dataset based on a
trivia quiz game. As a result, our model achieved state-of-the-art results on
all datasets. The source code of the proposed model is available online at
https://github.com/wikipedia2vec/wikipedia2vec.
