Implement SkipGram with Negative Sampling, Subsampling and SubwordEmbedding(FastText) using character n-grams in word2vec.py

referenced by Piotr Bojanowski∗, Edouard Grave∗, Armand Joulin, Tomas Mikolov, “Enriching Word Vectors with Subword Information”, ACL 2017

Korea University Information Retrieval(COSE 472) Assignment5

• this model use 2,3,4 and special grams

If you run "word2vec.py", you can train and test your models.

How to run

python word2vec.py [mode] [partition] [update_system] [sub_sampling]

• mode

  • "SG" for skipgram only

• partition

  • "part" if you want to train on a part of corpus (fast training but worse performance)
  • "full" if you want to train on full corpus (better performance but very slow training)

• update_system

  • "NS" for Negative Sampling only

• sub_sampling

  • True or False

Result