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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

    Finally, top2vec searches the semantic space for word embeddings located near to the topic vector to ascertain the 'meaning' of the topic. [18] 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 ...

  4. 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.

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  6. BERT (language model) - Wikipedia

    en.wikipedia.org/wiki/BERT_(language_model)

    The hidden states of the last layer can then be used as contextual word embeddings. BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules: Tokenizer: This module converts a piece of English text into a sequence of integers ("tokens").

  7. 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.

  8. Paraphrasing (computational linguistics) - Wikipedia

    en.wikipedia.org/wiki/Paraphrasing...

    The autoencoder is trained to reproduce every vector in the full recursion tree, including the initial word embeddings. Given two sentences W 1 {\displaystyle W_{1}} and W 2 {\displaystyle W_{2}} of length 4 and 3 respectively, the autoencoders would produce 7 and 5 vector representations including the initial word embeddings.

  9. fastText - Wikipedia

    en.wikipedia.org/wiki/FastText

    Download QR code; Print/export Download as PDF; Printable version; In other projects ... fastText is a library for learning of word embeddings and text classification ...