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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]
Font embedding is the inclusion of font files inside an electronic document for display across different platforms.
An embedding, or a smooth embedding, is defined to be an immersion that is an embedding in the topological sense mentioned above (i.e. homeomorphism onto its image). [ 4 ] 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 .
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.
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]
Object Linking and Embedding (OLE) is a proprietary technology developed by Microsoft that allows embedding and linking to documents and other objects. For developers, it brought OLE Control Extension (OCX), a way to develop and use custom user interface elements.
Re. "Most new word embedding techniques rely on a neural network architecture instead of more traditional n-gram models and unsupervised learning"--Can someone rewrite this to indicate whether it is "rely on (a neural network architecture) instead of (more traditional n-gram models and unsupervised learning)" or "rely on (a neural network architecture (instead of more traditional n-gram models ...
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