word2vec4kor

• Word2vec for Korean
• Keynote: https://github.com/bage79/word2vec4kor/raw/master/docs/korean_embedding_20180224_KCD.pdf
• Install & Run: https://github.com/bage79/word2vec4kor/blob/master/INSTALL.md
  • corpus -> dataset -> word2vec(numpy format) -> visualization data(tensorboard format)

tensorboard_log word2vec visualization data

• tensorboard embedding(projector) file format
• window size: 1
• Total unique words: 10,000
• Tokenized: white-space
• Embedding Dimension: 300
• Skip-Gram + Negative Sampling + Subsampling

  
  mkdir ~/workspace
  cd ~/workspace

git clone https://github.com/bage79/word2vec4kor tensorboard --logdir=~/workspace/word2vec4kor/tensorboard_log

![demo](https://github.com/bage79/word2vec4kor/raw/master/img/demo.png)

### `ko.wikipedia.org.sentences` raw corpus 
- from `https://ko.wikipedia.org`
- Total sentences: about 3,115,431
```angular2html
wget https://gitlab.com/bage79/nlp4kor-ko.wikipedia.org/raw/master/data/ko.wikipedia.org.sentences.gz
gzip -d ko.wikipedia.org.sentences.gz

Tips

Download korean Wikipedia dump file

• https://dumps.wikimedia.org/kowiki/20180220/

Parse dump file(mediawiki format) to text file

• https://pypi.python.org/pypi/mediawiki-parser/

Word2vec open source

• https://github.com/theeluwin/pytorch-sgns