word2vec

问题 I understand how word2vec works. I want to use word2vec(skip-gram) as input for RNN. Input is embedding word vector. Output is also embedding word vector generated by RNN. Here’s question! How can I convert the output vector to one hot word vector? I need inverse matrix of embeddings but I don’t have! 回答1: The output of an RNN is not an embedding. We convert the output from the last layer in an RNN cell into a vector of vocabulary_size by multiplying with an appropriate matrix. Take a look at

问题 What I want exactly is to cluster words and phrases, e.g. knitting/knit loom/loom knitting/weaving loom/rainbow loom/home decoration accessories/loom knit/knitting loom/...And I don'd have corpus while I have only the words/phrases. Could I use a pre-trained model like the one from GoogleNews/Wikipedia/... to realise it? I am trying now to use Gensim to load GoogleNews pre-trained model to get phrases similarity. I've been told that The GoogleNews model includes vectors of phrases and words.

问题 I would like to use a pre-trained word2vec model in Spacy to encode titles by (1) mapping words to their vector embeddings and (2) perform the mean of word embeddings. To do this I use the following code: import spacy nlp = spacy.load('myspacy.bioword2vec.model') sentence = "I love Stack Overflow butitsalsodistractive" avg_vector = nlp(sentence).vector Where nlp(sentence).vector (1) tokenizes my sentence with white-space splitting, (2) vectorizes each word according to the dictionary provided

问题 I'd start saying i'm quite new to Keras and machine learning in general. I'm trying to build an "experimental" model consisting of two parts: An "encoder" which takes a string (containing a long series of attributes, i'm using the DBLP-ACM dataset), builds an embedding of the words of this string (word2vec), and encodes them in a vector (bidirectional LSTM). A trainable model which takes 3 vectors in input (result of model 1) and uses the triplet loss as loss function (i already defined it,

问题 I would like to ask more about Word2Vec: I am currently trying to build a program that check for the embedding vectors for a sentence. While at the same time, I also build a feature extraction using sci-kit learn to extract the lemma 0, lemma 1, lemma 2 from the sentence. From my understanding; 1) Feature extractions : Lemma 0, lemma 1, lemma 2 2) Word embedding: vectors are embedded to each character (this can be achieved by using gensim word2vec(I have tried it)) More explanation: Sentence

问题 I am having trained a word vector model and now I'd like to do some operations on those vectors. Currently I try to figure out how to e.g. add up some vectors like below and then get some synonyms from the resulting vector. The problem is that model.findSynonyms(org.apache.spark.mllib.linalg.Vector, Int) is making problems since I only get Array[Float] from my model . This is why I try to create a DenseVector which itself now needs Array[Double] and the chaos is perfect - but take a look

问题 I have trained a word2vec model in tensorflow. But when I save the session, it only outputted model.ckpt.data / .index / .meta files. I was thinking of implementing KNN method in retrieving nearest words. I saw answers of using gensim, but how can I save my tensorflow word2vec model into .txt first? 回答1: Simply evaluate the embeddings matrix into a numpy array and write it to the file along with resolved words. Sample code: vocabulary_size = 50000 embedding_size = 128 # Assume your word to

问题 I'm getting this error "AttributeError: 'Word2Vec' object has no attribute 'index2word'" in following code in python. Anyone knows how can I solve it? Acctually "tfidf_weighted_averaged_word_vectorizer" throws the error. "obli.csv" contains line of sentences. Thank you. from feature_extractors import tfidf_weighted_averaged_word_vectorizer dataset = get_data2() corpus, labels = dataset.data, dataset.target corpus, labels = remove_empty_docs(corpus, labels) # print('Actual class label:',

问题 Does gensim Word2Vec have an option that is the equivalent of "training steps" in the TensorFlow word2vec example here: Word2Vec Basic? If not, what default value does gensim use? Is the gensim parameter iter related to training steps? The TensorFlow script includes this section. with tf.Session(graph=graph) as session: # We must initialize all variables before we use them. init.run() print('Initialized') average_loss = 0 for step in xrange(num_steps): batch_inputs, batch_labels = generate

问题 I am trying to apply word embedding on tweets. I was trying to create a vector for each tweet by taking the average of the vectors of the words present in the tweet as follow: def word_vector(tokens, size): vec = np.zeros(size).reshape((1, size)) count = 0. for word in tokens: try: vec += model_w2v[word].reshape((1, size)) count += 1. except KeyError: # handling the case where the token is not in vocabulary continue if count != 0: vec /= count return vec Next, when I try to Prepare word2vec