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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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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,...


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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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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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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 ...


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I can't really make much sense of Eisenstein's distinction between distributional and distributed. And I think in your question you actually mix up the two terms as well, as distributed semantics involve symbolic structures, whereas distributional semantics are numerical vectors according to his definition. EDIT: actually, he seems to mix it up himself there?...


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Having a sound understanding on language processing will help you understand all its concepts. This summarise must reads for NLP.


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I propose you try this. It's about modern Natural Language Processing, Computational Linguistics and Speech Recognition, including Embeddings methods.


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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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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.


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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 Word embeddings 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 ...


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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 ...


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There is a pre-trained language model called ProphetNet for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction. https://github.com/microsoft/ProphetNet Also, there are few variants on hugging face website as well https://huggingface.co/models?search=ProphetNet


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To give a statistician's answer, the distinction is empirical (embedding) versus theoretical (latent positions). You define a statistical model which has latent positions that you could then try to estimate, given data. Or, given data, you might simply find a vector representation of each object of interest in a way that makes sense for the applications ...


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