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Syntactic n-grams are intended to reflect syntactic structure more faithfully than linear n-grams, and have many of the same applications, especially as features in a vector space model. Syntactic n-grams for certain tasks gives better results than the use of standard n-grams, for example, for authorship attribution. [12]
An n-gram is a sequence of n adjacent symbols in particular order. [1] The symbols may be n adjacent letters (including punctuation marks and blanks), syllables , or rarely whole words found in a language dataset; or adjacent phonemes extracted from a speech-recording dataset, or adjacent base pairs extracted from a genome.
In natural language processing a w-shingling is a set of unique shingles (therefore n-grams) each of which is composed of contiguous subsequences of tokens within a document, which can then be used to ascertain the similarity between documents. The symbol w denotes the quantity of tokens in each shingle selected, or solved for.
Decision tree learning – Machine learning algorithm; Ensemble learning – Statistics and machine learning technique; Gradient boosting – Machine learning technique; Non-parametric statistics – Type of statistical analysis; Randomized algorithm – Algorithm that employs a degree of randomness as part of its logic or procedure
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. [1]
The limited n-gram length used in SMT's n-gram language models caused a loss of context. NMT systems overcome this by not having a hard cut-off after a fixed number of tokens and by using attention to choosing which tokens to focus on when generating the next token. [37]: 900–901
In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents.