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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]
The idea of skip-gram is that the vector of a word should be close to the vector of each of its neighbors. The idea of CBOW is that the vector-sum of a word's neighbors should be close to the vector of the word. In the original publication, "closeness" is measured by softmax, but the framework allows other ways to measure closeness.
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.
Automatic vectorization, a compiler optimization that transforms loops to vector operations; Image tracing, the creation of vector from raster graphics; Word embedding, mapping words to vectors, in natural language processing
After embedding, the vector representation is normalized using a LayerNorm operation, outputting a 768-dimensional vector for each input token. After this, the representation vectors are passed forward through 12 Transformer encoder blocks, and are decoded back to 30,000-dimensional vocabulary space using a basic affine transformation layer.
Some reference management software include support for automatic embedding and (re)formatting of references in Word processor programs. This table lists this type of support for Microsoft Word, Pages, Apache OpenOffice / LibreOffice Writer, the LaTeX editors Kile and LyX, and Google Docs.
The Chicago Manual of Style is published in hardcover and online. The online edition includes the searchable text of the 16th through 18th—its most recent—editions with features such as tools for editors, a citation guide summary, and searchable access to a Q&A, where University of Chicago Press editors answer readers' style questions.
The underlying idea that "a word is characterized by the company it keeps" was popularized by Firth in the 1950s. [3] The distributional hypothesis is the basis for statistical semantics. Although the Distributional Hypothesis originated in linguistics, [4] [5] it is now receiving attention in cognitive science especially regarding the context ...