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The bag-of-words model (BoW) is a model of text which uses a representation of text that is based on an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity.
Word2vec can use either of two model architectures to produce these distributed representations of words: continuous bag of words (CBOW) or continuously sliding skip-gram. In both architectures, word2vec considers both individual words and a sliding context window as it iterates over the corpus.
It is a refinement over the simple bag-of-words model, by allowing the weight of words to depend on the rest of the corpus. It was often used as a weighting factor in searches of information retrieval, text mining, and user modeling. A survey conducted in 2015 showed that 83% of text-based recommender systems in digital libraries used tf–idf. [2]
The Washington Metro 4 ft 8 + 1 ⁄ 4 in (1,429 mm), the gauge is 1 ⁄ 4 inch (6 mm) less than 4 ft 8 + 1 ⁄ 2 in (1,435 mm) The trams in Nuremberg for one time nominally used 1,432 mm (4 ft 8 + 3 ⁄ 8 in), the gauge is 1 ⁄ 8 inch (3 mm) less than 4 ft 8 + 1 ⁄ 2 in (1,435 mm) The MTR uses 1,432 mm (4 ft 8 + 3 ⁄ 8 in) on most lines.
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Just Words. If you love Scrabble, you'll love the wonderful word game fun of Just Words. Play Just Words free online! By Masque Publishing
During World War II, the NC&StL found itself unable to order more diesel locomotives to handle the increased passenger traffic. [2] Officials decided to go for steam power; they accepted a proposal by the American Locomotive Company (ALCO) for a streamlined 4-8-4 J3 locomotive similar to the Norfolk and Western J class locomotives, (a design rejected by the Louisville and Nashville Railroad (L ...
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