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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.
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.
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 word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA, top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or ...
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.
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]
Font embedding, inclusion of font files inside an electronic document; Embedding, machine learning technique (or resulting representation) that maps complex, high-dimensional data into a lower-dimensional vector space of numerical vectors Word embedding, the embedding method when applied to words