A tool for learning vector representations of words and entities from Wikipedia
Project description
Wikipedia2Vec
Wikipedia2Vec is a tool used for obtaining embeddings (vector representations) of words and entities from Wikipedia. It is developed and maintained by Studio Ousia.
This tool enables you to learn embeddings of words and entities simultaneously, and places similar words and entities close to one another in a continuous vector space. Embeddings can be easily trained by a single command with a publicly available Wikipedia dump as input. This tool has been used in several state-of-the-art NLP models such as entity linking, named entity recognition, entity relatedness, and question answering.
Documentation and pretrained embeddings are available online at http://wikipedia2vec.github.io/.
Reference
If you use Wikipedia2Vec in a scientific publication, please cite the following paper:
Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, Wikipedia2Vec: An Optimized Implementation for Learning Embeddings from Wikipedia.
@article{yamada2018wikipedia2vec,
title={Wikipedia2Vec: An Optimized Implementation for Learning Embeddings from Wikipedia},
author={Yamada, Ikuya and Asai, Akari and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu},
journal={arXiv preprint 1812.06280},
year={2018}
}
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