Keep us posted on your discoveries. It would be interesting to see how different the embedding is to word2vec trained on a different corpus. I imagine borrowed words like "python" are clustered with programming languages rather than snakes in this case.
As a side note, not really having looked too deeply into word2vec, does word2vec capture multiple meanings? If so, how?
Starting training using file 10m.txt
Vocab size: 305432
Words in train file: 565170189
Alpha: 0.000045 Progress: 99.91% Words/thread/sec: 107.57k
real 174m19.955s
user 1315m35.661s
sys 3m27.011s
Comments
Keep us posted on your discoveries. It would be interesting to see how different the embedding is to word2vec trained on a different corpus. I imagine borrowed words like "python" are clustered with programming languages rather than snakes in this case.
As a side note, not really having looked too deeply into word2vec, does word2vec capture multiple meanings? If so, how?
All done, results are very promising! Examples are too long so here is one below, and this gist has more: https://gist.github.com/binarymax/6befa448df3f5fd6dba9
Enter word or sentence (EXIT to break): startup