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  2. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. 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.

  3. tf–idf - Wikipedia

    en.wikipedia.org/wiki/Tf–idf

    In information retrieval, tf–idf (also TF*IDF, TFIDF, TF–IDF, or Tf–idf), short for term frequency–inverse document frequency, is a measure of importance of a word to a document in a collection or corpus, adjusted for the fact that some words appear more frequently in general. [1]

  4. Bag-of-words model - Wikipedia

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

    The bag-of-words model (BoW) is a model of text which uses a representation of text that is based on an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity.

  5. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Word frequency has been extracted. 1080 Text Classification 2012 [98] ... JSONL & CSV text files & SQLite database) Climate news DB, ... Web platform with Python, R, ...

  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. 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...

  8. Vector space model - Wikipedia

    en.wikipedia.org/wiki/Vector_space_model

    It contains incremental (memory-efficient) algorithms for term frequency-inverse document frequency, latent semantic indexing, random projections and latent Dirichlet allocation. Weka. Weka is a popular data mining package for Java including WordVectors and Bag Of Words models. Word2vec. Word2vec uses vector spaces for word embeddings.

  9. Brown Corpus - Wikipedia

    en.wikipedia.org/wiki/Brown_Corpus

    The Brown University Standard Corpus of Present-Day American English, better known as simply the Brown Corpus, is an electronic collection of text samples of American English, the first major structured corpus of varied genres. This corpus first set the bar for the scientific study of the frequency and distribution of word categories in ...