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  2. Lexical density - Wikipedia

    en.wikipedia.org/wiki/Lexical_density

    The lexical density is the proportion of content words (lexical items) in a given discourse. It can be measured either as the ratio of lexical items to total number of words, or as the ratio of lexical items to the number of higher structural items in the sentences (for example, clauses).

  3. APA style - Wikipedia

    en.wikipedia.org/wiki/APA_style

    APA style (also known as APA format) is a writing style and format for academic documents such as scholarly journal articles and books. It is commonly used for citing sources within the field of behavioral and social sciences , including sociology, education, nursing, criminal justice, anthropology, and psychology.

  4. Word count - Wikipedia

    en.wikipedia.org/wiki/Word_count

    Word count is commonly used by translators to determine the price of a translation job. Word counts may also be used to calculate measures of readability and to measure typing and reading speeds (usually in words per minute). When converting character counts to words, a measure of 5 or 6 characters to a word is generally used for English. [1]

  5. Keyword density - Wikipedia

    en.wikipedia.org/wiki/Keyword_density

    When calculating the density of a keyword phrase, the formula would be (/), [1] where Nwp is the number of words in the phrase. So, for example, for a four-hundred word page about search engine optimization where "search engine optimization" is used four times, the keyword phrase density is (4*3/400)*100 or 3 percent.

  6. Zipf's law - Wikipedia

    en.wikipedia.org/wiki/Zipf's_law

    For example, in the Brown Corpus of American English text, the word "the" is the most frequently occurring word, and by itself accounts for nearly 7% of all word occurrences (69,971 out of slightly over 1

  7. Word n-gram language model - Wikipedia

    en.wikipedia.org/wiki/Word_n-gram_language_model

    To prevent a zero probability being assigned to unseen words, each word's probability is slightly lower than its frequency count in a corpus. To calculate it, various methods were used, from simple "add-one" smoothing (assign a count of 1 to unseen n -grams, as an uninformative prior ) to more sophisticated models, such as Good–Turing ...

  8. tf–idf - Wikipedia

    en.wikipedia.org/wiki/Tf–idf

    Like the bag-of-words model, it models a document as a multiset of words, without word order. It is a refinement over the simple bag-of-words model, by allowing the weight of words to depend on the rest of the corpus. It was often used as a weighting factor in searches of information retrieval, text mining, and user modeling.

  9. Coleman–Liau index - Wikipedia

    en.wikipedia.org/wiki/Coleman–Liau_index

    The Coleman–Liau index is calculated with the following formula: = L is the average number of letters per 100 words and S is the average number of sentences per 100 words. Note that the multiplication operator is often omitted (as is common practice in mathematical formulas when it is clear that multiplication is implied).