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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]
Reading by using phonics is often referred to as decoding words, sounding-out words or using print-to-sound relationships.Since phonics focuses on the sounds and letters within words (i.e. sublexical), [13] it is often contrasted with whole language (a word-level-up philosophy for teaching reading) and a compromise approach called balanced literacy (the attempt to combine whole language and ...
Hexspeak is a novelty form of variant English spelling using the hexadecimal digits. Created by programmers as memorable magic numbers, hexspeak words can serve as a clear and unique identifier with which to mark memory or data.
Font embedding in word processors is not widely supported nor interoperable. [4] [5] For example, if a .rtf file made in Microsoft Word is opened in LibreOffice Writer, it will usually remove the embedded fonts. [citation needed]
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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.
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In other words, the domain of an embedding is diffeomorphic to its image, and in particular the image of an embedding must be a submanifold. An immersion is precisely a local embedding , i.e. for any point x ∈ M {\displaystyle x\in M} there is a neighborhood x ∈ U ⊂ M {\displaystyle x\in U\subset M} such that f : U → N {\displaystyle f ...