wikipedia2vec-SM002583 0.2
A tool for learning vector representations of words and entities from Wikipedia
Keywords: wikipedia,embedding,wikipedia2vec

Wikipedia2Vec
=============

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Wikipedia2Vec is a tool used for obtaining embeddings (or vector representations) of words and entities (i.e., concepts that have corresponding pages in Wikipedia) from Wikipedia.
It is developed and maintained by [Studio Ousia](http://www.ousia.jp).

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 implements the [conventional skip-gram model](https://en.wikipedia.org/wiki/Word2vec) to learn the embeddings of words, and its extension proposed in [Yamada et al. (2016)](https://arxiv.org/abs/1601.01343) to learn the embeddings of entities.
This tool has been used in several state-of-the-art NLP models such as [entity linking](https://arxiv.org/abs/1601.01343), [named entity recognition](http://www.aclweb.org/anthology/I17-2017), [knowledge graph completion](https://www.aaai.org/Papers/AAAI/2019/AAAI-ShahH.6029.pdf), [entity relatedness](https://arxiv.org/abs/1601.01343), and [question answering](https://arxiv.org/abs/1803.08652).

This tool has been tested on Linux, Windows, and macOS.

An empirical comparison between Wikipedia2Vec and existing embedding tools (i.e., FastText, Gensim, RDF2Vec, and Wiki2vec) is available [here](https://arxiv.org/abs/1812.06280).

Documentation and pretrained embeddings for 12 languages (English, Arabic, Chinese, Dutch, French, German, Italian, Japanese, Polish, Portuguese, Russian, and Spanish) are available online at [http://wikipedia2vec.github.io/](http://wikipedia2vec.github.io/).

Basic Usage
-----------

Wikipedia2Vec can be installed via PyPI:

```bash
% pip install wikipedia2vec
```

With this tool, embeddings can be learned by running a *train* command with a Wikipedia dump as input.
For example, the following commands download the latest English Wikipedia dump and learn embeddings from this dump:

```bash
% wget https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2
% wikipedia2vec train enwiki-latest-pages-articles.xml.bz2 MODEL_FILE
```

Then, the learned embeddings are written to *MODEL\_FILE*.
Note that this command can take many optional parameters.
Please refer to [our documentation](https://wikipedia2vec.github.io/wikipedia2vec/commands/) for further details.

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 Tool for Learning Embeddings of Words and Entities from Wikipedia](https://arxiv.org/abs/1812.06280).

```text
@article{yamada2018wikipedia2vec,
  title={Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia},
  author={Yamada, Ikuya and Asai, Akari and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu},
  journal={arXiv preprint 1812.06280},
  year={2018}
}
```

License
-------

[Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0)