word-embedding

I have a Word2Vec model which was trained on a huge corpus. While using this model for Neural network application I came across quite a few "Out of Vocabulary" words. Now I need to find word embeddings for these "Out of Vocabulary" words. So I did some googling and found that Facebook has recently released a FastText library for this. Now my question is how can I convert my existing word2vec model or Keyedvectors to FastText model? FastText is able to create vectors for subword fragments by including those fragments in the initial training, from the original corpus. Then, when encountering an

I want to construct word embeddings for documents using word2vec tool. I know how to find a vector embedding corresponding to a single word(unigram). Now, I want to find a vector for a bigram. Is it possible to do using word2vec? If yes, how? The following snippet will get you the vector representation of a bigram. Note that the bigram you want to convert to a vector needs to have an underscore instead of a space between the words, e.g. bigram2vec(unigrams, "this report") is wrong, it should be bigram2vec(unigrams, "this_report") . For more details on generating the unigrams, please see the

I have a Word2Vec model which is trained in Gensim . How can I use it in Tensorflow for Word Embeddings . I don't want to train Embeddings from scratch in Tensorflow. Can someone tell me how to do it with some example code? Let's assume you have a dictionary and inverse_dict list, with index in list corresponding to most common words: vocab = {'hello': 0, 'world': 2, 'neural':1, 'networks':3} inv_dict = ['hello', 'neural', 'world', 'networks'] Notice how the inverse_dict index corresponds to the dictionary values. Now declare your embedding matrix and get the values: vocab_size = len(inv_dict)