Matches in DBpedia 2016-04 for { ?s ?p "Word2vec is a group of related models that are used to produce so-called word embeddings. These models are shallow, two-layer neural networks, that are trained to reconstruct linguistic contexts of words: the network is shown a word, and must guess which words occurred in adjacent positions in an input text. The order of the remaining words is not important (bag-of-words assumption).After training, word2vec models can be used to map each word to a vector of typically several hundred elements, which represent that word's relation to other words. This vector is the neural network's hidden layer.Word2vec relies on either skip-grams or continuous bag of words (CBOW) to create neural word embeddings. It was created by a team of researchers led by Tomas Mikolov at Google. The algorithm has been subsequently analysed and explained by other researchers."@en }
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- Word2vec abstract "Word2vec is a group of related models that are used to produce so-called word embeddings. These models are shallow, two-layer neural networks, that are trained to reconstruct linguistic contexts of words: the network is shown a word, and must guess which words occurred in adjacent positions in an input text. The order of the remaining words is not important (bag-of-words assumption).After training, word2vec models can be used to map each word to a vector of typically several hundred elements, which represent that word's relation to other words. This vector is the neural network's hidden layer.Word2vec relies on either skip-grams or continuous bag of words (CBOW) to create neural word embeddings. It was created by a team of researchers led by Tomas Mikolov at Google. The algorithm has been subsequently analysed and explained by other researchers.".
- Q22673982 abstract "Word2vec is a group of related models that are used to produce so-called word embeddings. These models are shallow, two-layer neural networks, that are trained to reconstruct linguistic contexts of words: the network is shown a word, and must guess which words occurred in adjacent positions in an input text. The order of the remaining words is not important (bag-of-words assumption).After training, word2vec models can be used to map each word to a vector of typically several hundred elements, which represent that word's relation to other words. This vector is the neural network's hidden layer.Word2vec relies on either skip-grams or continuous bag of words (CBOW) to create neural word embeddings. It was created by a team of researchers led by Tomas Mikolov at Google. The algorithm has been subsequently analysed and explained by other researchers.".