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  2. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    e. 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. Once trained, such a model can detect synonymous ...

  3. Word embedding - Wikipedia

    en.wikipedia.org/wiki/Word_embedding

    e. 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. Tomáš Mikolov - Wikipedia

    en.wikipedia.org/wiki/Tomáš_Mikolov

    Mikolov obtained his PhD in Computer Science from Brno University of Technology for his work on recurrent neural network-based language models. [1] [2] He is the lead author of the 2013 paper that introduced the Word2vec technique in natural language processing [3] and is an author on the FastText architecture.

  5. BERT (language model) - Wikipedia

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

    BERT (language model) Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1][2] It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture.

  6. Gensim - Wikipedia

    en.wikipedia.org/wiki/Gensim

    Website. radimrehurek.com /gensim /. Gensim is an open-source library for unsupervised topic modeling, document indexing, retrieval by similarity, and other natural language processing functionalities, using modern statistical machine learning. Gensim is implemented in Python and Cython for performance. Gensim is designed to handle large text ...

  7. Transformer (deep learning architecture) - Wikipedia

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

    Transformer architecture is now used in many generative models that contribute to the ongoing AI boom. 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]

  8. fastText - Wikipedia

    en.wikipedia.org/wiki/FastText

    Website. fasttext.cc. 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 create an unsupervised learning or supervised learning algorithm for obtaining vector representations for words. Facebook makes available pretrained models for ...

  9. Seq2seq - Wikipedia

    en.wikipedia.org/wiki/Seq2seq

    Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...