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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 main benefit of OLE is to add different kinds of data to a document from different applications, like a text editor and an image editor. This creates a Compound File Binary Format document and a master file to which the document makes reference. Changes to data in the master file immediately affect the document that references it.
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 .
Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words.
Font embedding is a controversial practice because it allows copyrighted fonts to be freely distributed. The controversy can be mitigated by only embedding the characters required to view the document (subsetting). This reduces file size but prohibits adding previously unused characters to the document.
Embedding, installing media into a text document to form a compound document <embed></embed> , a HyperText Markup Language (HTML) element that inserts a non-standard object into the HTML document Web embed , an element of a host web page that is substantially independent of the host page
The Web Embedding Fonts Tool, or WEFT, is Microsoft's utility for generating embeddable web fonts.. WEFT is used by webmasters to create 'font objects' that are linked to their web pages so that users using Microsoft's Internet Explorer web browser will see the pages displayed in the font style contained within the font object.
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.