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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] | |||