2018/12/15 by Ikuya Yamada, Akari Asai, Yamada, Ikuya +12 · 1 voice · 3 citations
Computer Science · Social Sciences · #Natural Language Processing Techniques #Topic Modeling #Wikis in Education and Collaboration #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.1812.06280
openalex publication_date 2018/12/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.