We present Wikipedia2Vec, an open source tool for learning embeddings of
words and entities from Wikipedia. This tool enables users to easily obtain
high-quality embeddings of words and entities from a Wikipedia dump with a
single command. The learned embeddings can be used as features in downstream
natural language processing (NLP) models. The tool can be installed via PyPI.
The source code, documentation, and pretrained embeddings for 12 major
languages can be obtained at http://wikipedia2vec.github.io.
Description
Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia
%0 Generic
%1 yamada2018wikipedia2vec
%A Yamada, Ikuya
%A Asai, Akari
%A Shindo, Hiroyuki
%A Takeda, Hideaki
%A Takefuji, Yoshiyasu
%D 2018
%K embeddings multimodal wikipedia
%T Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and
Entities from Wikipedia
%U http://arxiv.org/abs/1812.06280
%X We present Wikipedia2Vec, an open source tool for learning embeddings of
words and entities from Wikipedia. This tool enables users to easily obtain
high-quality embeddings of words and entities from a Wikipedia dump with a
single command. The learned embeddings can be used as features in downstream
natural language processing (NLP) models. The tool can be installed via PyPI.
The source code, documentation, and pretrained embeddings for 12 major
languages can be obtained at http://wikipedia2vec.github.io.
@misc{yamada2018wikipedia2vec,
abstract = {We present Wikipedia2Vec, an open source tool for learning embeddings of
words and entities from Wikipedia. This tool enables users to easily obtain
high-quality embeddings of words and entities from a Wikipedia dump with a
single command. The learned embeddings can be used as features in downstream
natural language processing (NLP) models. The tool can be installed via PyPI.
The source code, documentation, and pretrained embeddings for 12 major
languages can be obtained at http://wikipedia2vec.github.io.},
added-at = {2019-01-15T09:44:53.000+0100},
author = {Yamada, Ikuya and Asai, Akari and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu},
biburl = {https://www.bibsonomy.org/bibtex/2f68e4f6d73e93d06dc85e7ea66a60117/dallmann},
description = {Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia},
interhash = {e3ad2fa43cc1148991b7def2fc38a251},
intrahash = {f68e4f6d73e93d06dc85e7ea66a60117},
keywords = {embeddings multimodal wikipedia},
note = {cite arxiv:1812.06280},
timestamp = {2019-01-15T09:44:53.000+0100},
title = {Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and
Entities from Wikipedia},
url = {http://arxiv.org/abs/1812.06280},
year = 2018
}