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  2. Letter frequency - Wikipedia

    en.wikipedia.org/wiki/Letter_frequency

    The California Job Case was a compartmentalized box for printing in the 19th century, sizes corresponding to the commonality of letters. The frequency of letters in text has been studied for use in cryptanalysis, and frequency analysis in particular, dating back to the Arab mathematician al-Kindi (c. AD 801–873 ), who formally developed the method (the ciphers breakable by this technique go ...

  3. Bag-of-words model - Wikipedia

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

    A common alternative to using dictionaries is the hashing trick, where words are mapped directly to indices with a hashing function. [5] Thus, no memory is required to store a dictionary. Hash collisions are typically dealt via freed-up memory to increase the number of hash buckets [clarification needed]. In practice, hashing simplifies the ...

  4. Word n-gram language model - Wikipedia

    en.wikipedia.org/wiki/Word_n-gram_language_model

    A word n-gram language model is a purely statistical model of language. It has been superseded by recurrent neural network–based models, which have been superseded by large language models. [1] It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words.

  5. Sentence embedding - Wikipedia

    en.wikipedia.org/wiki/Sentence_embedding

    An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). [9] However, more elaborate solutions based on word vector quantization have also been proposed.

  6. Word embedding - Wikipedia

    en.wikipedia.org/wiki/Word_embedding

    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]

  7. 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.

  8. Sentence diagram - Wikipedia

    en.wikipedia.org/wiki/Sentence_diagram

    A sentence diagram is a pictorial representation of the grammatical structure of a sentence. The term "sentence diagram" is used more when teaching written language, where sentences are diagrammed. The model shows the relations between words and the nature of sentence structure and can be used as a tool to help recognize which potential ...

  9. Combinatorics on words - Wikipedia

    en.wikipedia.org/wiki/Combinatorics_on_words

    For example, the word "encyclopedia" is a sequence of symbols in the English alphabet, a finite set of twenty-six letters. Since a word can be described as a sequence, other basic mathematical descriptions can be applied. The alphabet is a set, so as one would expect, the empty set is a subset. In other words, there exists a unique word of ...