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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. 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]

  5. 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 ...

  6. Lexical analysis - Wikipedia

    en.wikipedia.org/wiki/Lexical_analysis

    Simple examples include semicolon insertion in Go, which requires looking back one token; concatenation of consecutive string literals in Python, [7] which requires holding one token in a buffer before emitting it (to see if the next token is another string literal); and the off-side rule in Python, which requires maintaining a count of indent ...

  7. 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]

  8. Sentence embedding - Wikipedia

    en.wikipedia.org/wiki/Sentence_embedding

    An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). [9] However, more elaborate solutions based on word vector quantization have also been proposed.

  9. Category:Articles with example Python (programming language ...

    en.wikipedia.org/wiki/Category:Articles_with...

    Pages in category "Articles with example Python (programming language) code" The following 200 pages are in this category, out of approximately 201 total. This list may not reflect recent changes .