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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.
In psycholinguistics, parsing involves not just the assignment of words to categories (formation of ontological insights), but the evaluation of the meaning of a sentence according to the rules of syntax drawn by inferences made from each word in the sentence (known as connotation). This normally occurs as words are being heard or read.
In languages that use inter-word spaces (such as most that use the Latin alphabet, and most programming languages), this approach is fairly straightforward. However, even here there are many edge cases such as contractions, hyphenated words, emoticons, and larger constructs such as URIs (which for some purposes may count as single tokens). A ...
A common use of WordNet is to determine the similarity between words. Various algorithms have been proposed, including measuring the distance among words and synsets in WordNet's graph structure, such as by counting the number of edges among synsets. The intuition is that the closer two words or synsets are, the closer their meaning.
Boilerplate text, or simply boilerplate, is any written text that can be reused in new contexts or applications without significant changes to the original. The term is used about statements, contracts, and source code , and is often used in the media pejoratively to refer to clichéd or unoriginal writing.
In linguistic morphology and information retrieval, stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form—generally a written word form. The stem need not be identical to the morphological root of the word; it is usually sufficient that related words map to the same stem, even if this ...
If only one previous word is considered, it is called a bigram model; if two words, a trigram model; if n − 1 words, an n-gram model. [2] Special tokens are introduced to denote the start and end of a sentence s {\displaystyle \langle s\rangle } and / s {\displaystyle \langle /s\rangle } .
In information theory, linguistics, and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.