| Line 35: | Line 35: | ||
==GloVe== | ==GloVe== | ||
| − | Invented by the Natural language processing group in standford | + | Invented by the Natural language processing group in standford [https://nlp.stanford.edu/projects/glove/]. Uses more conventional math instead of Neural Network "Black Magic" [https://www.quora.com/How-is-GloVe-different-from-word2vec]. Seems to perform just slightly less well than Word2vec and FastWord. |
== pre trained models == | == pre trained models == | ||
For a general explanation look here: [1]
Made by Google, uses Neural Net, performs good on semantics.
Included in the gensim package.
To install, just type
pip install gensim
into a command window.
Here are some of the things you can do with the model: [2]
Here is a bit of background information an an explanation how to train your own models: [3].
Made by Facebook based on word2vec. Better at capturing syntactic relations (like apparent ---> apparently) see here:
[4]
Pretrained model files are HUGE - this will be a problem on computers with less than 16GB Memory
Included in the gensim package.
To install, just type
pip install gensim
into a command window.
Documentation is here: [5]
Invented by the Natural language processing group in standford [6]. Uses more conventional math instead of Neural Network "Black Magic" [7]. Seems to perform just slightly less well than Word2vec and FastWord.