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

Abstract

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.

arXiv commentEMNLP 2020 (system demonstration)

References in corpus (7)

• Efficient Estimation of Word Representations in Vector Space
• Distributed Representations of Words and Phrases and their Compositionality
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• An Open-World Extension to Knowledge Graph Completion Models
• Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification
• On the Coherence of Fake News Articles
• Contextualized End-to-End Neural Entity Linking

Cited by in corpus (5)

• Is Machine Learning Speaking my Language? A Critical Look at the NLP-Pipeline Across 8 Human Languages
• Quantum-inspired canonical correlation analysis for exponentially large dimensional data
• Neural Attentive Bag-of-Entities Model for Text Classification
• On the Coherence of Fake News Articles
• ReSCo-CC: Unsupervised Identification of Key Disinformation Sentences