I am currently developing a text classification tool using Keras. It works (it works fine and I got up to 98.7 validation accuracy) but I can't wrap my head around about how exactly 1D-convolution ...

I'm building an RNN LSTM network to classify texts based on the writers' age (binary classification - young / adult). Seems like the network does not learn and suddenly starts overfitting: Red: ...

I am trying to build a translation network using embedding and RNN. I have trained a Gensim Word2Vec model and it is learning word associations pretty well. However, I couldn’t get my head around h...

In word embedding, what should be a good vector representation for the start_tokens _PAD, _UNKNOWN, _GO, _EOS?

I am confused about how to format my own pre-trained weights for Keras Embedding layer if I'm also setting mask_zero=True. Here's a concrete toy example. Suppose I have a vocabulary of 4 words [1,...

I'm training up an RNN with a very reduced set of word features, around 10,000. I was planning on starting with an embedding layer before adding RNNs, but it is very unclear to me what dimensionali...

I'm currently working with a Keras model which has a embedding layer as first layer. In order to visualize the relationships and similarity of words between each other I need a function that return...

I read the paper and googled as well if there is any good example of the learning method(or more likely learning procedure) For word2vec, suppose there is corpus sentence I go to school with lu...

I understand how to use the Keras Embedding layer in case there is a single text feature like in IMDB review classification. However, I am confused how to use the Embedding Layers when I have a Cla...

In my model, I use GloVe pre-trained embeddings. I wish to keep them non-trainable in order to decrease the number of model parameters and avoid overfit. However, I have a special symbol whose embe...

I'm attempting to load some pre-trained vectors into a gensim Word2Vec model, so they can be retrained with new data. My understanding is I can do the retraining with gensim.Word2Vec.train(). Howev...

At 15:10 of this video about fastText it mentions syntactic analogy and semantic analogy. But I am not sure what the difference is between them. Could anybody help explain the difference with exam...

I've been trying to understand the sample code with https://www.tensorflow.org/tutorials/recurrent which you can find at https://github.com/tensorflow/models/blob/master/tutorials/rnn/ptb/ptb_word_...

I am building TensorFlow model for NLP task, and I am using pretrained Glove 300d word-vector/embedding dataset. Obviously some tokens can't be resolved as embeddings, because were not included in...

Suppose we're training a neural network model to learn the mapping from the following input to output, where the output is Name Entity (NE). Input: EU rejects German call to boycott British lamb ....

In the paper that I am trying to implement, it says, In this work, tweets were modeled using three types of text representation. The first one is a bag-of-words model weighted by tf-idf (term ...

When I try to create a word2vec model (skipgram with negative sampling) I received 3 files as output as follows. word2vec (File) word2vec.syn1nef.npy (NPY file) word2vec.wv.syn0.npy (NPY file) I...

Objective : Identifying class label using user entered question (like Question Answer system). Data extracted from Big PDF file, and need to predict page number based on user input. Majorly used...

If I have a text string to be vectorized, how should I handle numbers inside it? Or if I feed a Neural Network with numbers and words, how can I keep the numbers as numbers? I am planning on maki...

I followed the tutorial here: (https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html) However, I modified the code to be able to save the generated model through h5py. Thus...

I need to ask few questions regarding word embeddings.....could be basic. When we convert a one-hot vector of a word for instance king [0 0 0 1 0] into an embedded vector E = [0.2, 0.4, 0.2, 0.2]....

from gensim.models import word2vec sentences = word2vec.Text8Corpus('TextFile') model = word2vec.Word2Vec(sentences, size=200, min_count = 2, workers = 4) print model['king'] Is the output vecto...

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...

The question is simple. Which of the CBOW & skip-gram works better for a big dataset? (And the answer for small dataset follows.) I am confused since, by Mikolov himself, [Link] Skip-gram: ...

I am trying to learn the word representation of the imdb dataset "from scratch" through the TensorFlow tf.nn.embedding_lookup() function. If I understand it correctly, I have to set up an embedding...