Embedding vs Latent Space
Due to Machine Learning's recent and rapid renaissance, and the fact that it draws from many distinct areas of mathematics, statistics, and computer science, it often has a number of different terms for the same or similar concepts.
"Latent space" and "embedding" both refer to an (often lower-dimensional) ...
When it comes to normal layman terms "latent space" means it cannot be accessed, thus we have no direct control over it. We can only manipulate it indirectly, while "Embeddings" can be obtained directly. We can use deterministic operations or transformations to convert an object into its corresponding embedding space.
There is no marked difference between ...
Though word-embedding is primarily a language modeling tool, it also acts as a feature extraction method because it helps transform raw data (characters in text documents) to a meaningful alignment of word vectors in the embedding space that the model can work with more effectively (than other traditional methods such as TF-IDF, Bag of Words, etc, on a large ...
Instead of using the Embedding() layer directly, you can create a new bertEmbedding() layer and use it instead.
# Sample code
# Model architecture
# Custom BERT layer
bert_output = BertLayer(n_fine_tune_layers=10)(bert_inputs)
# Build the rest of the classifier
dense = tf.keras.layers.Dense(256, activation='relu')(bert_output)
pred = tf.keras.layers....
Shakespeare once said "A rose by any other name would smell as sweet" (Romeo and Juliet). Words are just labels we attach to ideas for convenience. By using one hot we remain tied to the letter sequence r,o,s,e, and some other structure must take on the responsibility of attaching the context of sweetness to it.
Word embeddings learn a multi-dimensional ...
Do individual dimensions in vector space have meaning?
IIRC, some dimensions are interpretable, but in general this is not the case. Also it is debatable as to wether it is actually learning the actual representation or just an approximation of it. But in any case its not very reliable outside from some edge cases.
If we picked out only a single ...
You can try to read about MUSE (Multilingual Unsupervised and Supervised Embeddings) by Facebook. You can read it from its Github or this article. They also provide the FastText dictionary format (.vec file) for some languages.
Their original paper shows how it aligns the vector of words from two different languages:
I think you guys are playing on semantics.
If you consider feature extraction to be an unlearned preprocessing step to get inputs for your model, then no, word embeddings are not a feature extraction technique (examples here would be BoW counts, n-gram features, etc)
If you consider feature extraction to be any form of conversion from text to a set of ...
Unlike in skip-gram, the reason similar words have similar embeddings in CBOW is because the words show up in the same contexts of other skipped words.
lets assume two words $e_i$ and $e_j$ pop up in the exact same context of some word $e_k$ with 3 other context words as well. An example would be:
He leaped over the truck
He jumped over the truck
I ended up finding this article which does what I'm looking for.
Below is the portion of code I adapted for my needs
from sklearn.metrics.pairwise import cosine_similarity
import tensorflow_hub as hub
import tensorflow as tf
elmo = hub.Module("https://tfhub.dev/google/elmo/2", trainable=True)
If you have to move a lot of data around during training (like retrieving batches from disk/network/what have you), it's much faster to do so as a rank-3 tensor of [batches, documents, indices] than as a rank-4 tensor of [batches, documents, indices, vectors]. In this case, while the embedding is O(1) wherever you put it, it's more efficient to do so as part ...
I have found a good answer in this blog post The Transformer: Attention Is All You Need:
we learn a “word embedding” which is a smaller real-valued vector representation of the word that carries some information about the word. We can do this using nn.Embedding in Pytorch, or, more generally speaking, by multiplying our one-hot vector with a learned weight ...
No, neither Word2Vec nor GloVe is used as Transformers are a newer class of algorithms. Word2Vec and GloVe are based on static word embeddings while Transformers are based on dynamic word embeddings.
The embeddings are trained from scratch.
I finally grasped the concept of word embedding. Thanks to @nbro, after reading the 2 articles s/he recommended
What Are Word Embeddings for Text? and
the 1st article gives me a good idea about the big picture of the Word Embeddings; whereas the 2nd article is actually the one which clears my mind.
I am an visual person, I understand ...
The specific term you are looking for is "word embedding" and not just "embedding".
How to numerically represent textual data?
Neural networks (typically) require as inputs (and produce as outputs) numerical data (i.e. numbers, vectors, matrices, or higher-dimensional arrays). So, when processing textual data, we first need to encode (or ...
There are multiple ways to get word embedding from a corpus.
Count Vectorizer: You can use the CountVectorizer() from sklearn.feature_extraction.text and then use the fit_transform() if the corpus has been converted into a list of sentences
TF-IDF Vectorizer: You can use the TfidfVectorizer from sklearn.feature_extraction.text and then again use the ...
It doesn't drops rows or columns, it acts directly on scalars. The Dropout Layer keras documentation explains it and illustrates it with an example :
The Dropout layer randomly sets input units to 0 with a frequency of rate
After an Dense Layer, the Dropout inputs are directly the outputs of the Dense layer neurons, as you said. After your embedding layer, ...
I think you should use Keras embedding layer. It will be too easier than what you are doing.
Create Embedding Matrix
add matrix to embedding layer while building model.
You will find detailed article
I think what you are looking for is an RNN network (Either LSTM or GRU) with the many-to-many topology.
Clearly your input is the sentences (or to be more precise, the an embedding of your sentences, because you cannot feed the raw text to the network). then for each sentence you want to assign a value, which means for n inputs, you ...
I think word embeddings are overkill in this particular case.
My suggestion would be to go for a simple dictionary based approach: compose sets of semantically related words, and then use those to expand your query terms. This might take a bit longer to set up, but has several advantages:
simplicity: you can't make many mistakes with this
transparency: you ...
The information you are probably missing is that word embeddings are learned on the basis of context. For example, you might try to predict a vector for a word from the wordvectors of the other words in the same sentence.
This way word vectors of words that occur in similar contexts will turn out to be similar. You can think of it as word vectors not ...
The subword-based embedding is rather visual and easily understandable. However, the autoencoder embedding is what machines understand the componential meaning of words.
1) An autoencoder embedding layer can be trained together with other layers to fit with the relation of data in dataset.
2) Or the embedding layer can be kept unchanged as used as a ...
BERT is deterministic. There is no variation unless you parse your tokens differently in succeeding runs. Here is the original paper the model architecture is based off of Transformer Paper. Note that in every layer, the only operations used for the most part are matrix multiplications, concatenations, basic ops, and layer normalizations, all of which are ...
For cross-language word representation the trend now is:
ELMoForManyLangs: git_repo original_paper_March_2018
MUSE by Facebook: git_repo original_paper_January_2018
Remember that you can also do the task in 2 steps:
Translate the words to a reference language (e.g english), then represent each one of them using any word representation model (in the ...
If you only need the vector space as a way to obtain a similarity measure, you may want to consider a distance measure instead. Similarity and distance are inversely related: identical words have maximum similarity or zero distance, and as the similarity decreases, the distance increases.
For instance, the Wagner-Fischer algorithm computes the edit ...
Almost, but no. When you maximize that objective function, you do so by adjusting the parameters $\phi$ and $\theta$. After you're done with training, you can use your word embeddings for other NLP tasks. You don't, however, do any prediction directly from the skip-gram model.
To maximize the first term, co-occuring words must have large inner products. ...
The expression "latent space" explicitly indicates that the space is associated with the mathematical concept of an hidden (or latent) variable, which cannot be observed directly, but only indirectly.
The expression "embedding space" refers to a vector space that represents an original space of inputs (e.g. images or words). For example, in the case of "...
No, the word vectors are not one-hot encodings. Yes, they are learned.
The purpose of the word2vec model is actually to learn dense, semantically meaningful encodings for words. That is, if your words are $d$-dimensional vectors, then each word's position in this vector space says something about what that word means. This is because word2vec learns to ...
Actually, LSTM is not used to get word2vec. Indeed, word2vec is extracted from corpus of words using MLP (Multi Layer Perceptron). There is a great tutorial on how to extract word2wec:
After representing word as vectors, you feed your text to LSTM in a deep architecture which the last ...
Adding to Colin's answer; using word embedding tend to be much more robust that one-hot vectors. Consider the the following two sentences:
The desk has a book on it.
The table has a book on it.
These two sentences are almost identical in meaning. If we were to using word embeddings, the vectors 'desk' and 'table' would be very close together. The ...