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docs/index.md

@@ -1,9 +1,10 @@
-<h1 id="main_title">Wikipedia2Vec</h1>
+# Wikipedia2Vec
+
 ---
 
 <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>
 
-### Introduction
+## Introduction
 
 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).
@@ -15,31 +16,31 @@ This tool implements the [conventional skip-gram model](https://en.wikipedia.org
 
 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).
 
-### Pretrained Embeddings
+## Pretrained Embeddings
 
 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).
 
-### Use Cases
+## Use Cases
 
 Wikipedia2Vec has been applied to the following tasks:
 
-* 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).
-* 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).
-* Question answering: [Yamada et al., 2017](https://arxiv.org/abs/1803.08652), [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
-* Entity typing: [Yamada et al., 2018](https://arxiv.org/abs/1806.02960).
-* 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).
-* Relation classification: [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
-* Paraphrase detection: [Duong et al., 2018](https://ieeexplore.ieee.org/abstract/document/8606845).
-* 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/).
-* Fake news detection: [Singh et al., 2019](https://arxiv.org/abs/1906.11126), [Ghosal et al., 2020](https://arxiv.org/abs/2010.10836).
-* Plot analysis of movies: [Papalampidi et al., 2019](https://arxiv.org/abs/1908.10328).
-* Novel entity discovery: [Zhang et al., 2020](https://arxiv.org/abs/2002.00206).
-* Entity retrieval: [Gerritse et al., 2020](https://link.springer.com/chapter/10.1007%2F978-3-030-45439-5_7).
-* Deepfake detection: [Zhong et al., 2020](https://arxiv.org/abs/2010.07475).
-* Conversational information seeking: [Rodriguez et al., 2020](https://arxiv.org/abs/2005.00172).
-* Query expansion: [Rosin et al., 2020](https://arxiv.org/abs/2012.12065).
-
-### References
+- 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).
+- 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).
+- Question answering: [Yamada et al., 2017](https://arxiv.org/abs/1803.08652), [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
+- Entity typing: [Yamada et al., 2018](https://arxiv.org/abs/1806.02960).
+- 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).
+- Relation classification: [Poerner et al., 2020](https://arxiv.org/abs/1911.03681).
+- Paraphrase detection: [Duong et al., 2018](https://ieeexplore.ieee.org/abstract/document/8606845).
+- 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/).
+- Fake news detection: [Singh et al., 2019](https://arxiv.org/abs/1906.11126), [Ghosal et al., 2020](https://arxiv.org/abs/2010.10836).
+- Plot analysis of movies: [Papalampidi et al., 2019](https://arxiv.org/abs/1908.10328).
+- Novel entity discovery: [Zhang et al., 2020](https://arxiv.org/abs/2002.00206).
+- Entity retrieval: [Gerritse et al., 2020](https://link.springer.com/chapter/10.1007%2F978-3-030-45439-5_7).
+- Deepfake detection: [Zhong et al., 2020](https://arxiv.org/abs/2010.07475).
+- Conversational information seeking: [Rodriguez et al., 2020](https://arxiv.org/abs/2005.00172).
+- Query expansion: [Rosin et al., 2020](https://arxiv.org/abs/2012.12065).
+
+## References
 
 If you use Wikipedia2Vec in a scientific publication, please cite the following paper:
 
@@ -86,6 +87,6 @@ Ikuya Yamada, Hiroyuki Shindo, [Neural Attentive Bag-of-Entities Model for Text
 }
 ```
 
-### License
+## License
 
 [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0)

mkdocs.yml

@@ -3,19 +3,20 @@ site_description: Wikipedia2Vec is a tool used for obtaining embeddings (vector
 repo_url: https://github.com/wikipedia2vec/wikipedia2vec
 edit_uri: ""
 site_author: Studio Ousia
-pages:
+nav:
     - Home: index.md
     - Introduction: intro.md
     - User Guide:
         - Installation: install.md
         - Learning Embeddings: commands.md
         - API Usage: usage.md
-    - Pretrained Embeddings: pretrained.md
+    - Embeddings: pretrained.md
+    - Demo: "https://wikipedia2vec.github.io/demo/"
 extra_css:
     - css/extra.css
 theme:
     name: mkdocs
     custom_dir: docs/custom_theme
 markdown_extensions:
     - toc:
-        permalink: 
+        permalink: "#"