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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 ]
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 ]
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.
A thesaurus is composed by at least three elements: 1-a list of words (or terms), 2-the relationship amongst the words (or terms), indicated by their hierarchical relative position (e.g. parent/broader term; child/narrower term, synonym, etc.), 3-a set of rules on how to use the thesaurus.
fastText is a library for learning of word embeddings and text classification created by Facebook's AI Research (FAIR) lab. [3] [4] [5] [6] The model allows one to ...
Web embed, an element of a host web page that is substantially independent of the host page; Font embedding, inclusion of font files inside an electronic document; Word embedding, a text representation technique used in natural language processing. Data representations generated through feature learning
[30] [31] [32] Even though most of traditional word-embedding techniques conflate words with multiple meanings into a single vector representation, they still can be used to improve WSD. [33] A simple approach to employ pre-computed word embeddings to represent word senses is to compute the centroids of sense clusters.
Distributional semantics [1] is a research area that develops and studies theories and methods for quantifying and categorizing semantic similarities between linguistic items based on their distributional properties in large samples of language data.