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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.
A reference dimension is a dimension on an engineering drawing provided for information only. [1] Reference dimensions are provided for a variety of reasons and are often an accumulation of other dimensions that are defined elsewhere [2] (e.g. on the drawing or other related documentation). These dimensions may also be used for convenience to ...
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.
The dimension of a vector space is the number of vectors in any basis for the space, i.e. the number of coordinates necessary to specify any vector. This notion of dimension (the cardinality of a basis) is often referred to as the Hamel dimension or algebraic dimension to distinguish it from other notions of dimension.
In engineering and science, dimensional analysis is the analysis of the relationships between different physical quantities by identifying their base quantities (such as length, mass, time, and electric current) and units of measurement (such as metres and grams) and tracking these dimensions as calculations or comparisons are performed.
A grammar checker will find each sentence in a text, look up each word in the dictionary, and then attempt to parse the sentence into a form that matches a grammar. Using various rules, the program can then detect various errors, such as agreement in tense, number, word order, and so on. It is also possible to detect some stylistic problems ...
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 ]
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 ...
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