Questions tagged [word-embedding]

For questions related to word embeddings, which are vector representations of words.

24 questions with no upvoted or accepted answers
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What should the dimension of the input be for text summarization?

I am trying to build a model for extractive text summarization using keras sequential layers. I am having a hard time trying to understand how to input my x data. Should it be an array of documents ...
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28 views

Why embedding layer is used in the character-level Natural Language Processing models

Problem Background I am working with a problem, which requires a character-level, deep learning model. Previously I was working with word-level deep NLP (Natural Language Processing) models, and in ...
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Why does all of NLP literature use noise contrastive estimation loss for negative sampling instead of sampled softmax loss?

A sampled softmax function is like a regular softmax but randomly selects a given number of 'negative' samples. This is difference than NCE Loss, which doesn't use a softmax at all, it uses a ...
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56 views

Is there a good book or paper on word embeddings?

Is there a good and modern book that focuses on word embeddings and their applications? It would also be ok to provide the name of a paper that provides a good overview of word embeddings.
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38 views

How can I feed any word into a neural network?

I am working on an Intent detection problem for a chatbot in Java. So I need to convert words from String to a double[] format. I tried using wordToVec(deeplearning4j), but it does not return a vector ...
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Doubt on formulating cost function for GloVe

I'm reading the notes here and have a doubt on page 2 ("Least squares objective" section). The probability of a word $j$ occurring in the context of word $i$ is $$Q_{ij}=\frac{\exp(u_j^Tv_i)}{\sum_{w=...
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124 views

How does FastText support online learning?

I'm using FastText pre-trained-embedding for tackling a classification task, but I saw it supports also online training (incremental training) for adding domain-specific corpus. How does it work? ...
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25 views

How homographs is an NLP task can be treated?

A homograph - is a word that shares the same written form as another word but has a different meaning. They can be even different parts of speech. For example: close(verb) - close(adverb) lead(verb)...
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Is it good practice to save NLP Transformer based pre-trained models into file system in production environment

I have developed a multi label classifier using BERT. I'm leveraging Hugging Face Pytorch implementation for transformers. I have saved the pretrained model into the file directory in dev environment. ...
1
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1answer
33 views

How can I create an embedding layer to convert words to a vector space from scratch?

For an upcoming project, I am trying to build a neural network for classifying text from scratch, without the use of libraries. This requires an embedding layer, or a way to convert words to some ...
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16 views

How many spectrogram frames per input character does text-to-speech (TTS) system Tacotron-2 generate?

I've been reading on Tacotron-2, a text-to-speech system, that generates speech just-like humans (indistinguishable from humans) using the GitHub https://github.com/Rayhane-mamah/Tacotron-2. I'm very ...
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30 views

Creating Text Features using word2vec

My task is to classify some texts. I have used word2vec to represent text words and I pass them to an LSTM as input. Taking into account that texts do not contain the same number of words, is it a ...
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51 views

How get matrix of word embeddings in FastText of gensim?

I try get the matrix embedding of my model but I can't because although this code gives no error it never ends running. The code is: ...
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22 views

Is there a way to parallelize GloVe cooccur function?

I would like to create a GloVe word embedding on a very large corpus (trillions of words). However, creating the co-occurence matrix with the GloVe cooccur script is projected to take weeks. Is there ...
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33 views

Reference request: one-hot encoding outperforming random orthogonal encoding

I experimented with a CNN operating on texts encoded as sequences of character vectors, where characters are encoded as one-hot vectors in one embedding and as random unit length pairwise orthogonal ...
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79 views

Multiple embedding layers?

How would one go about inputting multiple high dimensionality categorical columns using TensorFlow's Embedding Feature Columns? Does that even make sense to do? For example: for a car price predictor,...
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19 views

Creating a zero element in embedding space

I have some variable length input vectors for my own use case of a 'stylistic transfer'-esque process, and I am wondering if anyone knows of a way to engineer an input that maps to a 0 element in ...
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46 views

How could I learn tree paths given word embeddings?

I need to map from a vector space representation onto a tree structure. A possible solution: given a word vector as input, produce a path in the tree from the root down to the node that most closely ...
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Automated Scoring (non-english language) Using BERT

i'm a student and i'm new to NLP. I want to build an Automated Scoring system which is in Indonesian Language using BERT. The system is expected to be able to measure the similarity of an answer(e.g: ...
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22 views

What is the right way to set the dimension of the word representation in SkipGram word2vec?

I know that in word2vec each word has two word representations; one for the center word and one for the context word. What is the right way to set the dimension of the center word using SkipGram, ...
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30 views

Which word representation should I use in word2vec?

I know that in word2vec each word has two word representations; one for center word and one for context word. In NLP, when I want to set the words of a text as an input of an LSTM/RNN using word2vec, ...
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16 views

Why word embedding such as word2vec is not used as the output layer of a seq2seq decoder?

It would make sense to make the decoder predict a smaller embedding vector instead of softmax over a large dictionary. The word having the most cosine similarity with the output embedding could be ...
0
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1answer
67 views

Real time ticket similarity

I'm dealing with a "ticket similarity task". Every time new tickets arrive at the help desk (customer service), I need to compare them and find out about similar ones. In this way, once the operator ...
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21 views

Understanding how continuous bag of words method learns embedded representations

I'm reading notes on word vectors here. Specifically, I'm referring to section 4.2 on page 7. First, regarding points 1 to 6 - here's my understanding: If we have a vocabulary $V$, the naive way to ...