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Approximation is a key word generally employed within the title of a directive, for example the Trade Marks Directive of 16 December 2015 serves "to approximate the laws of the Member States relating to trade marks". [11] The European Commission describes approximation of law as "a unique obligation of membership in the European Union". [10]
The concept of approximation also applies to the World Wide Web and other forms of textual information available via digital media, though usually at the level of characters, not glyphs. Historically, the main cause of typographic approximation was a low quantity of glyphs (such as letterforms and symbols) available for printing
These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus . Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.
PDF is a standard for encoding documents in an "as printed" form that is portable between systems. However, the suitability of a PDF file for archival preservation depends on options chosen when the PDF is created: most notably, whether to embed the necessary fonts for rendering the document; whether to use encryption; and whether to preserve additional information from the original document ...
PDF — Open standard for document exchange. ISO standards include PDF/X (eXchange), PDF/A (Archive), PDF/E (Engineering), ISO 32000 (PDF), PDF/UA (Accessibility) and PDF/VT (Variable data and transactional printing). PDF is readable on almost every platform with free or open source readers. Open source PDF creators are also available ...
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]
A word n-gram language model is a purely statistical model of language. It has been superseded by recurrent neural network–based models, which have been superseded by large language models. [1] It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words.
In the zeroth-order example above, the quantity "a few" was given, but in the first-order example, the number "4" is given. A first-order approximation of a function (that is, mathematically determining a formula to fit multiple data points) will be a linear approximation, straight line with a slope: a polynomial of degree 1. For example: