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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

    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.

  4. fastText - Wikipedia

    en.wikipedia.org/wiki/FastText

    fastText is a library for learning of word embeddings and text classification created by ... Several papers describe the techniques used by fastText. [9] [10] [11 ...

  5. BERT (language model) - Wikipedia

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

    Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary, whereas BERT takes into account the context for each occurrence of a given word. For instance, whereas the vector for "running" will have the same word2vec vector representation for both of its occurrences in the ...

  6. 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]

  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. Learned sparse retrieval - Wikipedia

    en.wikipedia.org/wiki/Learned_sparse_retrieval

    Learned sparse retrieval or sparse neural search is an approach to Information Retrieval which uses a sparse vector representation of queries and documents. [1] It borrows techniques both from lexical bag-of-words and vector embedding algorithms, and is claimed to perform better than either alone.

  9. List of steganography techniques - Wikipedia

    en.wikipedia.org/wiki/List_of_steganography...

    Hidden messages distributed, according to a certain rule or key, as smaller parts (e.g. words or letters) among other words of a less suspicious cover text. This particular form of steganography is called a null cipher. Messages written in Morse code on yarn and then knitted into a piece of clothing worn by a courier. [1]