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  2. 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 ...

  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. 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.

  5. Feature learning - Wikipedia

    en.wikipedia.org/wiki/Feature_learning

    Word2vec is a word embedding technique which learns to represent words through self-supervision over each word and its neighboring words in a sliding window across a large corpus of text. [28] The model has two possible training schemes to produce word vector representations, one generative and one contrastive. [27]

  6. Word-sense disambiguation - Wikipedia

    en.wikipedia.org/wiki/Word-sense_disambiguation

    For each context window, MSSA calculates the centroid of each word sense definition by averaging the word vectors of its words in WordNet's glosses (i.e., short defining gloss and one or more usage example) using a pre-trained word-embedding model. These centroids are later used to select the word sense with the highest similarity of a target ...

  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. WordNet - Wikipedia

    en.wikipedia.org/wiki/WordNet

    A number of WordNet-based word similarity algorithms are implemented in a Perl package called WordNet::Similarity, [21] and in a Python package called NLTK. [22] Other more sophisticated WordNet-based similarity techniques include ADW, [23] whose implementation is available in Java. WordNet can also be used to inter-link other vocabularies. [24]

  9. 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]