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For a general explanation look here: | For a general explanation look here: | ||
[https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/] | [https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/] | ||
As wordvector algorithms | |||
==Word2vec== | ==Word2vec== | ||
Made by Google, uses Neural Net, performs good on semantics. | Made by Google, uses Neural Net, performs good on semantics. | ||
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* [https://github.com/3Top/word2vec-api#where-to-get-a-pretrained-models https://github.com/3Top/word2vec-api Mostly GloVe, some word2vec, English, Trained on News, Wikipedia, Twitter] | * [https://github.com/3Top/word2vec-api#where-to-get-a-pretrained-models https://github.com/3Top/word2vec-api Mostly GloVe, some word2vec, English, Trained on News, Wikipedia, Twitter] | ||
* [https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md: Fasttext, all imaginable languages, trained on Wikipedia] | * [https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md: Fasttext, all imaginable languages, trained on Wikipedia] | ||
* [https://radimrehurek.com/gensim/scripts/glove2word2vec.html https://radimrehurek.com/gensim/scripts/glove2word2vec.html convert between GloVe and Word2Vec Format] | |||
* [https://levyomer.wordpress.com/2014/04/25/dependency-based-word-embeddings/ https://levyomer.wordpress.com/2014/04/25/dependency-based-word-embeddings/ an interesting approach that gives similarities between syntaktically equivalent words] | |||
Revision as of 17:53, 8 May 2017
General Information on word embeddings
For a general explanation look here: [1]
As wordvector algorithms
Word2vec
Made by Google, uses Neural Net, performs good on semantics.
Installation + getting started:
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].
Fastword
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
Installation + getting started:
Included in the gensim package.
To install, just type
pip install gensim
into a command window.
Documentation is here: [5]
GloVe
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.
pre trained models
- https://github.com/Kyubyong/wordvectors: Word2Vec and FastText, Multiple languages, no english, trained on Wikipedia
- https://github.com/3Top/word2vec-api Mostly GloVe, some word2vec, English, Trained on News, Wikipedia, Twitter
- https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md: Fasttext, all imaginable languages, trained on Wikipedia
- https://radimrehurek.com/gensim/scripts/glove2word2vec.html convert between GloVe and Word2Vec Format
- https://levyomer.wordpress.com/2014/04/25/dependency-based-word-embeddings/ an interesting approach that gives similarities between syntaktically equivalent words