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  2. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    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.

  3. Word embedding - Wikipedia

    en.wikipedia.org/wiki/Word_embedding

    Based on word2vec skip-gram, Multi-Sense Skip-Gram (MSSG) [35] performs word-sense discrimination and embedding simultaneously, improving its training time, while assuming a specific number of senses for each word. In the Non-Parametric Multi-Sense Skip-Gram (NP-MSSG) this number can vary depending on each word.

  4. Word n-gram language model - Wikipedia

    en.wikipedia.org/wiki/Word_n-gram_language_model

    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.

  5. Language model - Wikipedia

    en.wikipedia.org/wiki/Language_model

    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.

  6. File:Skip-gram.svg - Wikipedia

    en.wikipedia.org/wiki/File:Skip-gram.svg

    You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.

  7. Bag-of-words model - Wikipedia

    en.wikipedia.org/wiki/Bag-of-words_model

    The bag-of-words model (BoW) is a model of text which uses 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 .

  8. Struc2vec - Wikipedia

    en.wikipedia.org/wiki/Struc2vec

    struc2vec is a framework to generate node vector representations on a graph that preserve the structural identity. [1] In contrast to node2vec, that optimizes node embeddings so that nearby nodes in the graph have similar embedding, struc2vec captures the roles of nodes in a graph, even if structurally similar nodes are far apart in the graph.

  9. Bigram - Wikipedia

    en.wikipedia.org/wiki/Bigram

    A bigram or digram is a sequence of two adjacent elements from a string of tokens, which are typically letters, syllables, or words.A bigram is an n-gram for n=2.. The frequency distribution of every bigram in a string is commonly used for simple statistical analysis of text in many applications, including in computational linguistics, cryptography, and speech recognition.