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  2. 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]

  3. Font embedding - Wikipedia

    en.wikipedia.org/wiki/Font_embedding

    Both OpenOffice.org and LibreOffice support font embedding in the PDF export feature. [3] Font embedding in word processors is not widely supported nor interoperable. [4] [5] For example, if a .rtf file made in Microsoft Word is opened in LibreOffice Writer, it will usually remove the embedded fonts. [citation needed]

  4. Object Linking and Embedding - Wikipedia

    en.wikipedia.org/wiki/Object_Linking_and_Embedding

    For example, a desktop publishing system might send some text to a word processor or a picture to a bitmap editor using OLE. [1] The main benefit of OLE is to add different kinds of data to a document from different applications, like a text editor and an image editor.

  5. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    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. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.

  6. Word processor - Wikipedia

    en.wikipedia.org/wiki/Word_processor

    A word processor (WP) [1] [2] is a device or computer program that provides for input, editing, formatting, and output of text, often with some additional features.. Early word processors were stand-alone devices dedicated to the function, but current word processors are word processor programs running on general purpose computers.

  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. Rich Text Format - Wikipedia

    en.wikipedia.org/wiki/Rich_Text_Format

    The default text editor for macOS, TextEdit, can also view, edit and save RTF files as well as RTFD files, and uses the format as its default. As of July 2009, TextEdit has limited ability to edit RTF document margins. Much older Mac word processing application programs such as MacWrite and WriteNow had the same RTF abilities as TextEdit has.

  9. Comparison of text editors - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_text_editors

    Syntax highlighting: Displays text in different colors and fonts according to the category of terms. Function list : Lists all functions from current file in a window or sidebar and allows user to jump directly to the definition of that function for example by double-clicking on the function name in the list.