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‎docs/css/extra.css‎

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‎docs/index.md‎

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<h1 id="main_title">Wikipedia2Vec</h1>
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# Wikipedia2Vec
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---
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<a class="github-button" href="https://github.com/wikipedia2vec/wikipedia2vec" data-size="large" data-show-count="true" aria-label="Star wikipedia2vec/wikipedia2vec on GitHub">Star</a>
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### Introduction
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## Introduction
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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.
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It is developed and maintained by [Studio Ousia](http://www.ousia.jp).
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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).
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### Pretrained Embeddings
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## Pretrained Embeddings
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Pretrained embeddings for 12 languages (i.e., English, Arabic, Chinese, Dutch, French, German, Italian, Japanese, Polish, Portuguese, Russian, and Spanish) can be downloaded from [this page](pretrained.md).
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### Use Cases
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## Use Cases
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Wikipedia2Vec has been applied to the following tasks:
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* Entity linking: [Yamada et al., 2016](https://arxiv.org/abs/1601.01343), [Eshel et al., 2017](https://arxiv.org/abs/1706.09147), [Chen et al., 2019](https://arxiv.org/abs/1911.03834), [Poerner et al., 2020](https://arxiv.org/abs/1911.03681), [van Hulst et al., 2020](https://arxiv.org/abs/2006.01969).
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* Named entity recognition: [Sato et al., 2017](http://www.aclweb.org/anthology/I17-2017), [Lara-Clares and Garcia-Serrano, 2019](http://ceur-ws.org/Vol-2421/eHealth-KD_paper_6.pdf).
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* Question answering: [Yamada et al., 2017](https://arxiv.org/abs/1803.08652), [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
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* Entity typing: [Yamada et al., 2018](https://arxiv.org/abs/1806.02960).
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* Text classification: [Yamada et al., 2018](https://arxiv.org/abs/1806.02960), [Yamada and Shindo, 2019](https://arxiv.org/abs/1909.01259), [Alam et al., 2020](https://link.springer.com/chapter/10.1007/978-3-030-61244-3_9).
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* Relation classification: [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
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* Paraphrase detection: [Duong et al., 2018](https://ieeexplore.ieee.org/abstract/document/8606845).
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* Knowledge graph completion: [Shah et al., 2019](https://aaai.org/ojs/index.php/AAAI/article/view/4162), [Shah et al., 2020](https://www.aclweb.org/anthology/2020.textgraphs-1.9/).
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* Fake news detection: [Singh et al., 2019](https://arxiv.org/abs/1906.11126), [Ghosal et al., 2020](https://arxiv.org/abs/2010.10836).
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* Plot analysis of movies: [Papalampidi et al., 2019](https://arxiv.org/abs/1908.10328).
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* Novel entity discovery: [Zhang et al., 2020](https://arxiv.org/abs/2002.00206).
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* Entity retrieval: [Gerritse et al., 2020](https://link.springer.com/chapter/10.1007%2F978-3-030-45439-5_7).
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* Deepfake detection: [Zhong et al., 2020](https://arxiv.org/abs/2010.07475).
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* Conversational information seeking: [Rodriguez et al., 2020](https://arxiv.org/abs/2005.00172).
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* Query expansion: [Rosin et al., 2020](https://arxiv.org/abs/2012.12065).
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### References
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- Entity linking: [Yamada et al., 2016](https://arxiv.org/abs/1601.01343), [Eshel et al., 2017](https://arxiv.org/abs/1706.09147), [Chen et al., 2019](https://arxiv.org/abs/1911.03834), [Poerner et al., 2020](https://arxiv.org/abs/1911.03681), [van Hulst et al., 2020](https://arxiv.org/abs/2006.01969).
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- Named entity recognition: [Sato et al., 2017](http://www.aclweb.org/anthology/I17-2017), [Lara-Clares and Garcia-Serrano, 2019](http://ceur-ws.org/Vol-2421/eHealth-KD_paper_6.pdf).
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- Question answering: [Yamada et al., 2017](https://arxiv.org/abs/1803.08652), [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
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- Entity typing: [Yamada et al., 2018](https://arxiv.org/abs/1806.02960).
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- Text classification: [Yamada et al., 2018](https://arxiv.org/abs/1806.02960), [Yamada and Shindo, 2019](https://arxiv.org/abs/1909.01259), [Alam et al., 2020](https://link.springer.com/chapter/10.1007/978-3-030-61244-3_9).
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- Relation classification: [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
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- Paraphrase detection: [Duong et al., 2018](https://ieeexplore.ieee.org/abstract/document/8606845).
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- Knowledge graph completion: [Shah et al., 2019](https://aaai.org/ojs/index.php/AAAI/article/view/4162), [Shah et al., 2020](https://www.aclweb.org/anthology/2020.textgraphs-1.9/).
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- Fake news detection: [Singh et al., 2019](https://arxiv.org/abs/1906.11126), [Ghosal et al., 2020](https://arxiv.org/abs/2010.10836).
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- Plot analysis of movies: [Papalampidi et al., 2019](https://arxiv.org/abs/1908.10328).
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- Novel entity discovery: [Zhang et al., 2020](https://arxiv.org/abs/2002.00206).
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- Entity retrieval: [Gerritse et al., 2020](https://link.springer.com/chapter/10.1007%2F978-3-030-45439-5_7).
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- Deepfake detection: [Zhong et al., 2020](https://arxiv.org/abs/2010.07475).
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- Conversational information seeking: [Rodriguez et al., 2020](https://arxiv.org/abs/2005.00172).
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- Query expansion: [Rosin et al., 2020](https://arxiv.org/abs/2012.12065).
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## References
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If you use Wikipedia2Vec in a scientific publication, please cite the following paper:
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}
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```
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### License
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## License
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[Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0)

‎mkdocs.yml‎

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repo_url: https://github.com/wikipedia2vec/wikipedia2vec
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edit_uri: ""
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site_author: Studio Ousia
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pages:
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nav:
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- Home: index.md
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- Introduction: intro.md
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- User Guide:
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- Installation: install.md
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- Learning Embeddings: commands.md
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- API Usage: usage.md
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- Pretrained Embeddings: pretrained.md
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- Embeddings: pretrained.md
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- Demo: "https://wikipedia2vec.github.io/demo/"
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extra_css:
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- css/extra.css
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theme:
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name: mkdocs
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custom_dir: docs/custom_theme
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markdown_extensions:
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- toc:
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permalink:
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permalink: "#"

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