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The embeddings of entities in a large knowledge base (e.g., Wikipedia) arehighly beneficial for solving various natural language tasks that involve realworld knowledge. In this paper, we present Wikipedia2Vec, a Python-basedopen-source tool for learning the embeddings of words and entities fromWikipedia. The proposed tool enables users to learn the embeddings efficientlyby issuing a single command with a Wikipedia dump file as an argument. We alsointroduce a web-based demonstration of our tool that allows users to visualizeand explore the learned embeddings. In our experiments, our tool achieved astate-of-the-art result on the KORE entity relatedness dataset, and competitiveresults on various standard benchmark datasets. Furthermore, our tool has beenused as a key component in various recent studies. We publicize the sourcecode, demonstration, and the pretrained embeddings for 12 languages athttps://wikipedia2vec.github.io.

Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia