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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. Help:WordToWiki - Wikipedia

    en.wikipedia.org/wiki/Help:WordToWiki

    Open your document in Word, and "save as" an HTML file. Open the HTML file in a text editor and copy the HTML source code to the clipboard. Paste the HTML source into the large text box labeled "HTML markup:" on the html to wiki page. Click the blue Convert button at the bottom of the page.

  4. ELMo - Wikipedia

    en.wikipedia.org/wiki/ELMo

    ELMo (embeddings from language model) is a word embedding method for representing a sequence of words as a corresponding sequence of vectors. [1] It was created by researchers at the Allen Institute for Artificial Intelligence , [ 2 ] and University of Washington and first released in February, 2018.

  5. 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]

  6. Microsoft Office 2001 - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Office_2001

    This converter makes it possible to open and edit files created using Microsoft Word 98 or later on a Mac with only Word 6.0 installed. [6] Word Perfect 5 converter The Word Perfect 5 Converter allows for users to use Microsoft Word 2001 to open and save WordPerfect 5.0 and 5.1 files which is something that was not previously possible. [6]

  7. Sentence embedding - Wikipedia

    en.wikipedia.org/wiki/Sentence_embedding

    In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance [8] by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset.

  8. Controlled vocabulary - Wikipedia

    en.wikipedia.org/wiki/Controlled_vocabulary

    For example, in the Library of Congress Subject Headings [6] (a subject heading system that uses a controlled vocabulary), preferred terms—subject headings in this case—have to be chosen to handle choices between variant spellings of the same word (American versus British), choice among scientific and popular terms (cockroach versus ...

  9. Thesaurus - Wikipedia

    en.wikipedia.org/wiki/Thesaurus

    Thesaurus Linguae Latinae. A modern english 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 ...