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  2. Keyword (linguistics) - Wikipedia

    en.wikipedia.org/wiki/Keyword_(linguistics)

    In corpus linguistics a key word is a word which occurs in a text more often than we would expect to occur by chance alone. [1] Key words are calculated by carrying out a statistical test (e.g., loglinear or chi-squared) which compares the word frequencies in a text against their expected frequencies derived in a much larger corpus, which acts as a reference for general language use.

  3. Word recognition - Wikipedia

    en.wikipedia.org/wiki/Word_recognition

    The role of the frequency effect has been greatly incorporated into the learning process. [8] While the word analysis approach is extremely beneficial, many words defy regular grammatical structures and are more easily incorporated into the lexical memory by automatic word recognition.

  4. 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]

  5. K-5 (education) - Wikipedia

    en.wikipedia.org/wiki/K-5_(education)

    K-5 (pronounced "kay through five") is an American term for the education period from kindergarten to fifth grade.It receives equal amounts of criticism and support in the educational industry.

  6. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    The reasons for successful word embedding learning in the word2vec framework are poorly understood. Goldberg and Levy point out that the word2vec objective function causes words that occur in similar contexts to have similar embeddings (as measured by cosine similarity ) and note that this is in line with J. R. Firth's distributional hypothesis .

  7. Bag-of-words model - Wikipedia

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

    It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]

  8. Word learning biases - Wikipedia

    en.wikipedia.org/wiki/Word_learning_biases

    It is unclear if the word-learning constraints are specific to the domain of language, or if they apply to other cognitive domains. Evidence suggests that the whole object assumption is a result of an object's tangibility; children assume a label refers to a whole object because the object is more salient than its properties or functions. [7]

  9. 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 ...