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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. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA , top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or otherwise).

  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. Natural language processing - Wikipedia

    en.wikipedia.org/wiki/Natural_language_processing

    Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.

  6. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    In language modelling, ELMo (2018) was a bi-directional LSTM that produces contextualized word embeddings, improving upon the line of research from bag of words and word2vec. It was followed by BERT (2018), an encoder-only Transformer model. [35] In 2019 October, Google started using BERT to process search queries. [36]

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  8. Outline of natural language processing - Wikipedia

    en.wikipedia.org/wiki/Outline_of_natural...

    word2vec – models that were developed by a team of researchers led by Thomas Milkov at Google to generate word embeddings that can reconstruct some of the linguistic context of words using shallow, two dimensional neural nets derived from a much larger vector space. Festival Speech Synthesis System – CMU Sphinx speech recognition system –

  9. 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] ...