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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.
the set of 1-skip-2-grams includes all the bigrams (2-grams), and in addition the subsequences the in, rain Spain, in falls, Spain mainly, falls on, mainly the, and on plain. In skip-gram model, semantic relations between words are represented by linear combinations, capturing a form of compositionality.
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N-gram is actually the parent of a family of names term, where family members can be (depending on n numeral) 1-gram, 2-gram etc., or the same using spoken numeral prefixes. If Latin numerical prefixes are used, then n-gram of size 1 is called a "unigram", size 2 a "bigram" (or, less commonly, a "digram") etc.
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.
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]