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  2. Brevity law - Wikipedia

    en.wikipedia.org/wiki/Brevity_law

    Similarly, in a Latin corpus, he found a negative correlation between the number of syllables in a word and the frequency of its appearance. This observation says that the most frequent words in a language are the shortest, e.g. the most common words in English are: the , be (in different forms), to, of, and, a; all containing 1 to 3 phonemes.

  3. Document-term matrix - Wikipedia

    en.wikipedia.org/wiki/Document-term_matrix

    The output of this program is an alphabetical listing, by frequency of occurrence, of all word types which appeared in the text. Certain function words such as and, the, at, a, etc., were placed in a "forbidden word list" table, and the frequency of these words was recorded in a separate listing...

  4. tf–idf - Wikipedia

    en.wikipedia.org/wiki/Tf–idf

    The inverse document frequency is a measure of how much information the word provides, i.e., how common or rare it is across all documents. It is the logarithmically scaled inverse fraction of the documents that contain the word (obtained by dividing the total number of documents by the number of documents containing the term, and then taking ...

  5. Word list - Wikipedia

    en.wikipedia.org/wiki/Word_list

    Word frequency is known to have various effects (Brysbaert et al. 2011; Rudell 1993). Memorization is positively affected by higher word frequency, likely because the learner is subject to more exposures (Laufer 1997). Lexical access is positively influenced by high word frequency, a phenomenon called word frequency effect (Segui et al.).

  6. 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]

  7. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.

  8. Zipf's law - Wikipedia

    en.wikipedia.org/wiki/Zipf's_law

    A plot of the frequency of each word as a function of its frequency rank for two English language texts: Culpeper's Complete Herbal (1652) and H. G. Wells's The War of the Worlds (1898) in a log-log scale. The dotted line is the ideal law .

  9. Vector space model - Wikipedia

    en.wikipedia.org/wiki/Vector_space_model

    Candidate documents from the corpus can be retrieved and ranked using a variety of methods. Relevance rankings of documents in a keyword search can be calculated, using the assumptions of document similarities theory, by comparing the deviation of angles between each document vector and the original query vector where the query is represented as a vector with same dimension as the vectors that ...