word2vec

In this page, it is said that: [...] skip-gram inverts contexts and targets, and tries to predict each context word from its target word [...] However, looking at the training dataset it produces, the content of the X and Y pair seems to be interexchangeable, as those two pairs of (X, Y): (quick, brown), (brown, quick) So, why distinguish that much between context and targets if it is the same thing in the end? Also, doing Udacity's Deep Learning course exercise on word2vec , I wonder why they seem to do the difference between those two approaches that much in this problem: An alternative to

We have models for converting words to vectors (for example the word2vec model). Do similar models exist which convert sentences/documents into vectors, using perhaps the vectors learnt for the individual words? 1) Skip gram method: paper here and the tool that uses it, google word2vec 2) Using LSTM-RNN to form semantic representations of sentences. 3) Representations of sentences and documents . The Paragraph vector is introduced in this paper. It is basically an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences,

I have learned in some essays (Tomas Mikolov...) that a better way of forming the vector for a sentence is to concatenate the word-vector. but due to my clumsy in mathematics, I am still not sure about the details. for example, supposing that the dimension of word vector is m; and that a sentence has n words. what will be the correct result of concatenating operation? is it a row vector of 1 x m*n ? or a matrix of m x n ? please advise thanks There are at least three common ways to combine embedding vectors; (a) summing, (b) summing & averaging or (c) concatenating. So in your case, with

I have a set of embeddings trained with a neural network that has nothing to do with gensim's word2vec. I want to use these embeddings as the initial weights in gensim.Word2vec . Now what I did see is that I can model.load(SOME_MODEL) and then continue training, but it requires a gensim modle as input. Also reset_from() seems to only accept other gensim model. But in my case, I don't have a gensim model to start from, but a text file in word2vec format of embeddings. So how do I start transfer learning from an word2vec text file to gensim.Word2vec ? You can load other models using the key

I have a large pretrained Word2Vec model in gensim from which I want to use the pretrained word vectors for an embedding layer in my Keras model. The problem is that the embedding size is enormous and I don't need most of the word vectors (because I know which words can occure as Input). So I want to get rid of them to reduce the size of my embedding layer. Is there a way to just keep desired wordvectors (including the coresponding indices!), based on a whitelist of words? Thanks to this answer (I've changed the code a little bit to make it better). you can use this code for solving your

I have to make lexical graph with words within a corpus. For that, I need to make a program with word2vec. The thing is that I'm new at this. I've tried for 4 days now to find a way to use word2vec but I'm lost. My big problem is that I don't even know where to find the code in Java (I heard about deeplearning but I couldn't find the files on their website), how to integrate it in my project... One of the easiest way to embody the Word2Vec representation in your java code is to use deeplearning4j , the one you have mentioned. I assume you have already seen the main pages of the project. For