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In the continuous skip-gram architecture, the model uses the current word to predict the surrounding window of context words. [1] [2] The skip-gram architecture weighs nearby context words more heavily than more distant context words. According to the authors' note, [3] CBOW is faster while skip-gram does a better job for infrequent words.
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.
The model has been used in lexical substitution automation and prediction algorithms. One such algorithm developed by Oren Melamud, Omer Levy, and Ido Dagan uses the skip-gram model to find a vector for each word and its synonyms. Then, it calculates the cosine distance between vectors to determine which words will be the best substitutes. [2]
Gensim includes streamed parallelized implementations of fastText, [2] word2vec and doc2vec algorithms, [3] as well as latent semantic analysis (LSA, LSI, SVD), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), tf-idf and random projections.
Formally, a k-skip-n-gram is a length-n subsequence where the components occur at distance at most k from each other. For example, in the input text: the rain in Spain falls mainly on the plain. the set of 1-skip-2-grams includes all the bigrams (2-grams), and in addition the subsequences
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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.
To explain this observation, links have been shown between ESA and the generalized vector space model. [5] Gabrilovich and Markovitch replied to Anderka and Stein by pointing out that their experimental result was achieved using "a single application of ESA (text similarity)" and "just a single, extremely small and homogenous test collection of ...