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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.
Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. Word2vec was developed by Tomáš Mikolov and colleagues at Google and published in 2013. Word2vec represents a word as a high-dimension vector of numbers which capture relationships between words.
Some languages use 'n-gram' data, [7] which is massive and requires considerable processing power and I/O speed, for some extra detections. As such, LanguageTool is also offered as a web service that does the processing of 'n-grams' data on the server-side.
In the past, feature-based classifiers were also common, with features chosen from part-of-speech tags, sentence position, morphological information, etc. This is an O ( n ) {\displaystyle O(n)} greedy algorithm, so it does not guarantee the best possible parse or even a necessarily valid parse, but it is efficient. [ 21 ]
A grammar checker, in computing terms, is a program, or part of a program, that attempts to verify written text for grammatical correctness. Grammar checkers are most often implemented as a feature of a larger program, such as a word processor , but are also available as a stand-alone application that can be activated from within programs that ...
Paraphrase or paraphrasing in computational linguistics is the natural language processing task of detecting and generating paraphrases.Applications of paraphrasing are varied including information retrieval, question answering, text summarization, and plagiarism detection. [1]
BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the ...
In 1985, Halliday revised the denominator of the Ure formula and proposed the following to compute the lexical density of a sentence: [1] L d = The number of lexical items / The total number of clauses * 100 . In some formulations, the Halliday proposed lexical density is computed as a simple ratio, without the "100" multiplier. [2] [1]
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