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            "Natural Language :: English",
            "Programming Language :: Python",
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        "description": "Wikipedia2Vec\n=============\n\n[![Fury badge](https://badge.fury.io/py/wikipedia2vec.png)](http://badge.fury.io/py/wikipedia2vec)\n[![CircleCI](https://circleci.com/gh/wikipedia2vec/wikipedia2vec.svg?style=svg)](https://circleci.com/gh/wikipedia2vec/wikipedia2vec)\n\nWikipedia2Vec 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.\nIt is developed and maintained by [Studio Ousia](http://www.ousia.jp).\n\nThis 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.\nEmbeddings can be easily trained by a single command with a publicly available Wikipedia dump as input.\n\nThis 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.\nThis 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).\n\nThis tool has been tested on Linux, Windows, and macOS.\n\nAn empirical comparison between Wikipedia2Vec and existing embedding tools (i.e., FastText, Gensim, RDF2Vec, and Wiki2vec) is available [here](https://arxiv.org/abs/1812.06280).\n\nDocumentation 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/).\n\nBasic Usage\n-----------\n\nWikipedia2Vec can be installed via PyPI:\n\n```bash\n% pip install wikipedia2vec\n```\n\nWith this tool, embeddings can be learned by running a *train* command with a Wikipedia dump as input.\nFor example, the following commands download the latest English Wikipedia dump and learn embeddings from this dump:\n\n```bash\n% wget https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2\n% wikipedia2vec train enwiki-latest-pages-articles.xml.bz2 MODEL_FILE\n```\n\nThen, the learned embeddings are written to *MODEL\\_FILE*.\nNote that this command can take many optional parameters.\nPlease refer to [our documentation](https://wikipedia2vec.github.io/wikipedia2vec/commands/) for further details.\n\nReferences\n----------\n\nIf you use Wikipedia2Vec in a scientific publication, please cite the following paper:\n\nIkuya 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).\n\n```text\n@article{yamada2018wikipedia2vec,\n  title={Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia},\n  author={Yamada, Ikuya and Asai, Akari and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu},\n  journal={arXiv preprint 1812.06280},\n  year={2018}\n}\n```\n\nWikipedia2Vec is an official implementation of the embedding model proposed in the following paper:\n\nIkuya Yamada, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, [Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation](https://arxiv.org/abs/1601.01343).\n\n```text\n@inproceedings{yamada2016joint,\n  title={Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation},\n  author={Yamada, Ikuya and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu},\n  booktitle={Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning},\n  year={2016},\n  publisher={Association for Computational Linguistics},\n  doi={10.18653/v1/K16-1025},\n  pages={250--259}\n}\n```\n\nLicense\n-------\n\n[Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0)",
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