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 ...
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:
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)
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 ...
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 ...
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....
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. ...
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 ...
Input vector contains two concatenated parts. The low part represents the current word:
word in time t encoded using 1-of-N coding [...] - size of vector x is
equal to size of vocabulary V (this can be in practice 30 000-200 000) plus [...]
where, as you said, 1-of-N means (see here, 1-of-V):
If you have a fixed-size vocabulary of symbols with V ...