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  2. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    The CBOW can be viewed as a ‘fill in the blank’ task, where the word embedding represents the way the word influences the relative probabilities of other words in the context window. Words which are semantically similar should influence these probabilities in similar ways, because semantically similar words should be used in similar contexts.

  3. 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]

  4. fastText - Wikipedia

    en.wikipedia.org/wiki/FastText

    fastText is a library for learning of word embeddings and text classification created by Facebook's AI Research (FAIR) lab. [3] [4] [5] [6] The model allows one to ...

  5. WordNet - Wikipedia

    en.wikipedia.org/wiki/WordNet

    WordNet is a lexical database of semantic relations between words that links words into semantic relations including synonyms, hyponyms, and meronyms. The synonyms are grouped into synsets with short definitions and usage examples. It can thus be seen as a combination and extension of a dictionary and thesaurus.

  6. Conversion (word formation) - Wikipedia

    en.wikipedia.org/wiki/Conversion_(word_formation)

    In English, verbification typically involves simple conversion of a non-verb to a verb. The verbs to verbify and to verb, the first by derivation with an affix and the second by zero derivation, are themselves products of verbification (see autological word), and, as might be guessed, the term to verb is often used more specifically, to refer only to verbification that does not involve a ...

  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. Thesaurus - Wikipedia

    en.wikipedia.org/wiki/Thesaurus

    A thesaurus (pl.: thesauri or thesauruses), sometimes called a synonym dictionary or dictionary of synonyms, is a reference work which arranges words by their meanings (or in simpler terms, a book where one can find different words with similar meanings to other words), [1] [2] sometimes as a hierarchy of broader and narrower terms, sometimes simply as lists of synonyms and antonyms.

  9. Stemming - Wikipedia

    en.wikipedia.org/wiki/Stemming

    In linguistic morphology and information retrieval, stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form—generally a written word form. The stem need not be identical to the morphological root of the word; it is usually sufficient that related words map to the same stem, even if this ...