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  2. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    The reasons for successful word embedding learning in the word2vec framework are poorly understood. Goldberg and Levy point out that the word2vec objective function causes words that occur in similar contexts to have similar embeddings (as measured by cosine similarity) and note that this is in line with J. R. Firth's distributional hypothesis ...

  3. 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 ]

  4. Latent space - Wikipedia

    en.wikipedia.org/wiki/Latent_space

    Here are some commonly used embedding models: Word2Vec: [4] Word2Vec is a popular embedding model used in natural language processing (NLP). It learns word embeddings by training a neural network on a large corpus of text. Word2Vec captures semantic and syntactic relationships between words, allowing for meaningful computations like word analogies.

  5. 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.

  6. Top Trump officials set to meet with senior Russian officials ...

    www.aol.com/top-trump-officials-set-meet...

    Top Trump administration officials are set to meet with senior Russian officials to begin talks aimed at ending the war in Ukraine, according to multiple sources.

  7. 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 ...

  8. Today’s NYT ‘Strands’ Hints, Spangram and Answers for ...

    www.aol.com/today-nyt-strands-hints-spangram...

    Move over, Wordle, Connections and Mini Crossword—there's a new NYT word game in town! The New York Times' recent game, "Strands," is becoming more and more popular as another daily activity ...

  9. Trump administration ordered to restore removed CDC and FDA ...

    www.aol.com/news/trump-administration-ordered...

    A federal judge on Tuesday ordered U.S. health agencies to restore websites that they abruptly took offline in response to an executive order by President Donald Trump telling them to scrub ...