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  2. tf–idf - Wikipedia

    en.wikipedia.org/wiki/Tf–idf

    A number of term-weighting schemes have derived from tf–idf. One of them is TF–PDF (term frequency * proportional document frequency). [14] TF–PDF was introduced in 2001 in the context of identifying emerging topics in the media. The PDF component measures the difference of how often a term occurs in different domains.

  3. scikit-multiflow - Wikipedia

    en.wikipedia.org/wiki/Scikit-multiflow

    The scikit-multiflow library is implemented under the open research principles and is currently distributed under the BSD 3-clause license. scikit-multiflow is mainly written in Python, and some core elements are written in Cython for performance. scikit-multiflow integrates with other Python libraries such as Matplotlib for plotting, scikit-learn for incremental learning methods [4 ...

  4. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  5. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.

  6. 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.).

  7. Bigram - Wikipedia

    en.wikipedia.org/wiki/Bigram

    A bigram or digram is a sequence of two adjacent elements from a string of tokens, which are typically letters, syllables, or words.A bigram is an n-gram for n=2.. The frequency distribution of every bigram in a string is commonly used for simple statistical analysis of text in many applications, including in computational linguistics, cryptography, and speech recognition.

  8. Zipf's law - Wikipedia

    en.wikipedia.org/wiki/Zipf's_law

    A log-log plot of word frequency in the English Wikipedia (27 November 2006). 'Most popular words are "the", "of" and "and", as expected. Zipf's law corresponds to the middle linear portion of the curve, roughly following the green ( ⁠ 1 / x ⁠ ) line, while the early part is closer to the magenta ( ⁠ 1 / √ x ⁠ ) line while the later ...

  9. Stop word - Wikipedia

    en.wikipedia.org/wiki/Stop_word

    The phrase "stop word", which is not in Luhn's 1959 presentation, and the associated terms "stop list" and "stoplist" appear in the literature shortly afterward. [ 5 ] Although it is commonly assumed that stoplists include only the most frequent words in a language, it was C.J. Van Rijsbergen who proposed the first standardized list which was ...