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In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). [9] However, more elaborate solutions based on word vector quantization have also been proposed.
The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA , top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or otherwise).
Upper case variables represent the entire sentence, and not just the current word. For example, H is a matrix of the encoder hidden state—one word per column. S, T: S, decoder hidden state; T, target word embedding. In the Pytorch Tutorial variant training phase, T alternates between 2 sources depending on the level of teacher forcing used. T ...
One can tell if a sentence is center embedded or edge embedded depending on where the brackets are located in the sentence. [Joe believes [Mary thinks [John is handsome.]]] The cat [that the dog [that the man hit] chased] meowed. In sentence (1), all of the brackets are located on the right, so this sentence is right-embedded.
In the translation task, a sentence =, (consisting of tokens ) in the source language is to be translated into a sentence =, (consisting of tokens ) in the target language. The source and target tokens (which in the simple event are used for each other in order for a particular game ] vectors, so they can be processed mathematically.
Given a sentence with words, the autoencoder is designed to take 2 -dimensional word embeddings as input and produce an -dimensional vector as output. The same autoencoder is applied to every pair of words in S {\displaystyle S} to produce ⌊ m / 2 ⌋ {\displaystyle \lfloor m/2\rfloor } vectors.