  The embeddings of entities in a large knowledge base (e.g., Wikipedia) are
highly beneficial for solving various natural language tasks that involve real
world knowledge. In this paper, we present Wikipedia2Vec, a Python-based
open-source tool for learning the embeddings of words and entities from
Wikipedia. The proposed tool enables users to learn the embeddings efficiently
by issuing a single command with a Wikipedia dump file as an argument. We also
introduce a web-based demonstration of our tool that allows users to visualize
and explore the learned embeddings. In our experiments, our tool achieved a
state-of-the-art result on the KORE entity relatedness dataset, and competitive
results on various standard benchmark datasets. Furthermore, our tool has been
used as a key component in various recent studies. We publicize the source
code, demonstration, and the pretrained embeddings for 12 languages at
https://wikipedia2vec.github.io.
