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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 ]
In linguistics, center embedding is the process of embedding a phrase in the middle of another phrase of the same type. This often leads to difficulty with parsing which would be difficult to explain on grammatical grounds alone. The most frequently used example involves embedding a relative clause inside another one as in:
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 .
The text within the scope of the embedding formatting characters is not independent of the surrounding text. Also, characters within an embedding can affect the ordering of characters outside. Unicode 6.3 recognized that directional embeddings usually have too strong an effect on their surroundings and are thus unnecessarily difficult to use.
Embedding vectors created using the Word2vec algorithm have some advantages compared to earlier algorithms [1] such as those using n-grams and latent semantic analysis. GloVe was developed by a team at Stanford specifically as a competitor, and the original paper noted multiple improvements of GloVe over word2vec. [ 9 ]
Font embedding is the inclusion of font files inside an electronic document for ... For example, if a .rtf file made ... Text is available under the Creative Commons ...
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.
Place the image on the left side of the page. The article text that follows the image flows around the image, but there may be formatting issues with lists and indented text (see § Interaction between left-floating images and lists). center Place the image in the center of the page. The article text that follows the image is placed below the ...