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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]
LibreLogo is an integrated development environment (IDE) for computer programming in the programming language Python, which works like the language Logo using interactive vector turtle graphics. Its final output is a vector graphics rendition within the LibreOffice suite. It can be used for education and desktop publishing.
Turtle is an alternative to RDF/XML, the original syntax and standard for writing RDF. As opposed to RDF/XML, Turtle does not rely on XML and is generally recognized as being more readable and easier to edit manually than its XML counterpart. SPARQL, the query language for RDF, uses a syntax similar to Turtle for expressing query patterns.
Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.
Turtle graphics are often associated with the Logo programming language. [2] Seymour Papert added support for turtle graphics to Logo in the late 1960s to support his version of the turtle robot, a simple robot controlled from the user's workstation that is designed to carry out the drawing functions assigned to it using a small retractable pen set into or attached to the robot's body.
Text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table. [1] At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens ...
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.
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 ...