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  2. n-gram - Wikipedia

    en.wikipedia.org/wiki/N-gram

    An n-gram is a sequence of n adjacent symbols in particular order. [1] The symbols may be n adjacent letters (including punctuation marks and blanks), syllables , or rarely whole words found in a language dataset; or adjacent phonemes extracted from a speech-recording dataset, or adjacent base pairs extracted from a genome.

  3. Word n-gram language model - Wikipedia

    en.wikipedia.org/wiki/Word_n-gram_language_model

    Syntactic n-grams are n-grams defined by paths in syntactic dependency or constituent trees rather than the linear structure of the text. [ 9 ] [ 10 ] [ 11 ] For example, the sentence "economic news has little effect on financial markets" can be transformed to syntactic n -grams following the tree structure of its dependency relations : news ...

  4. 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.

  5. w-shingling - Wikipedia

    en.wikipedia.org/wiki/W-shingling

    In natural language processing a w-shingling is a set of unique shingles (therefore n-grams) each of which is composed of contiguous subsequences of tokens within a document, which can then be used to ascertain the similarity between documents. The symbol w denotes the quantity of tokens in each shingle selected, or solved for.

  6. Katz's back-off model - Wikipedia

    en.wikipedia.org/wiki/Katz's_back-off_model

    The equation for Katz's back-off model is: [2] (+) = {+ (+) (+) (+) > + (+)where C(x) = number of times x appears in training w i = ith word in the given context. Essentially, this means that if the n-gram has been seen more than k times in training, the conditional probability of a word given its history is proportional to the maximum likelihood estimate of that n-gram.

  7. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    Embedding vectors created using the Word2vec algorithm have some advantages compared to earlier algorithms [1] such as those using n-grams and latent semantic analysis. GloVe was developed by a team at Stanford specifically as a competitor, and the original paper noted multiple improvements of GloVe over word2vec. [ 9 ]

  8. ROUGE (metric) - Wikipedia

    en.wikipedia.org/wiki/ROUGE_(metric)

    ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, [1] is a set of metrics and a software package used for evaluating automatic summarization and machine translation software in natural language processing. The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced ...

  9. CMU Sphinx - Wikipedia

    en.wikipedia.org/wiki/CMU_Sphinx

    Sphinx is a continuous-speech, speaker-independent recognition system making use of hidden Markov acoustic models and an n-gram statistical language model. It was developed by Kai-Fu Lee. Sphinx featured feasibility of continuous-speech, speaker-independent large-vocabulary recognition, the possibility of which was in dispute at the time (1986).