Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia
This provides a practical tool for researchers and practitioners in natural language processing to efficiently generate embeddings for knowledge-based tasks, though it is incremental as it builds on existing embedding methods.
The authors tackled the problem of learning embeddings for words and entities from Wikipedia by developing Wikipedia2Vec, an efficient Python toolkit that achieved state-of-the-art results on the KORE entity relatedness dataset and competitive performance on other benchmarks.
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.