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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) ...

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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 ...

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An embedding is a representation of a word that can be used as a proxy for some of its linguistic properties. The 'human' representation of a word, a sequence of letters and other symbols, is not related at all to its meaning or use in actual text. It only serves as a look-up key into our cognitive language processing facility (however that actually works) ...

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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....

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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 "...

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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 ...

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An n-gram language model is a language model trained with n context words. This means you're not feeding the model a single word but n. This is why the dimension of the input layer is "context_size * embedding_dim" or "n * embedding_dims"

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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 ...

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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:

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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 Where ...

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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) def elmo_vectors(x): embeddings=elmo(x, signature="default",...

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Summing up a sequence of word vector maybe used in practice sometimes. However, the operation of addition is non-reversible, meaning that once you sum up a few numbers, you cannot get the original numbers back. However summing up a sequence of word vectors may work depending on your task. You should also normalize the values, or just use average value. For ...

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$USV^T$ refers to the result of the singular value decomposition (SVD). An $m \times n$ matrix $X$ can be written with the help of three matrices $$X = USV^T,$$ where $U$ is an $m \times m$ unitary matrix, $S$ is a diagonal $m \times n$ matrix with real entries called singular values, and $V$ is a unitary $n \times n$ matrix. The $^T$ is the Hermitian ...

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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 ...

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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 ...

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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 ...

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Simplified: Word Embeddings does not consider context, Language Models does. For e.g Word2Vec, GloVe, or fastText, there exists one fixed vector per word. Think of the following two sentences: The fish ate the cat. and The cat ate the fish. If you averaged their word embeddings, they would have the same vector, but, in reality, their meaning (semantic) ...

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A language model aims to estimate the probability of one or more words given the surrounding words. Given a sentence composed of $w_{1},...,w_{i-1},\_ , w_{i+1},..,w_{n}$, you can find which is the i-th missing word using a language model. In this way, you can estimate which is the most probable word using for example the conditional probability $P(w_i=w|w_1,... 2 Although we have had multiple similar questions (see here, here and here) and it seems to me that you focused on word embeddings (probably because you were not aware of the application of embeddings to other contexts), in addition to what is stated in the other answer, it's important to note that the concept of an embedding does not just apply to words. For ... 2 One-hot encoding is different than the concept of a word embedding, although both approaches use vectors to represent the objects (e.g. words). A one-hot vector contains one element that is 1 and all other elements are 0. So, for example, the vector$[0, 0, 1, 0]$is a one-hot vector, while the vector$[0, 2, 0.2, 0]\$ is not. (Given that the sum of all ...

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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 ...

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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, ...

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I think you should use Keras embedding layer. It will be too easier than what you are doing. Steps Create Embedding Matrix add matrix to embedding layer while building model. You will find detailed article https://www.cs.uaf.edu/2011/spring/cs641/lecture/04_05_modeling.html

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Briefly: I think what you are looking for is an RNN network (Either LSTM or GRU) with the many-to-many topology. Explanation: 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 ...

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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 ...

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But because the inputs have to have a fixed length Do they? Why? The go-to strategy would be to use an RNN (possibly with LSTM or GRUs, but probably not necessary) and train it to process input sequentially and output the final classification of the paragraph. This has the advantage of being able to take into account word order and constellations, as ...

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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 ...

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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 ...

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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 ...

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