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The theory achieves an exhaustive and discrete enumeration of the data points. There is a pigeonhole for each observation. Descriptive adequacy. The theory formally specifies rules accounting for all observed arrangements of the data. The rules produce all and only the well-formed constructs (relations) of the protocol space.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Set inclusions described by the Chomsky hierarchy. The Chomsky hierarchy in the fields of formal language theory, computer science, and linguistics, is a containment hierarchy of classes of formal grammars. A formal grammar describes how to form strings from a language's vocabulary (or alphabet) that are valid according to the language's syntax.
W-grammars are two-level grammars: they are defined by a pair of grammars, that operate on different levels: the hypergrammar is an attribute grammar, i.e. a set of context-free grammar rules in which the nonterminals may have attributes; and; the metagrammar is a context-free grammar defining possible values for these attributes.
Grammar rules may concern the use of clauses, phrases, and words. The term may also refer to the study of such rules, a subject that includes phonology, morphology, and syntax, together with phonetics, semantics, and pragmatics. There are, broadly speaking, two different ways to study grammar: traditional grammar and theoretical grammar.
Gradient well-formedness is a problem that arises in the analysis of data in generative linguistics, in which a linguistic entity is neither completely grammatical nor completely ungrammatical. A native speaker may judge a word, phrase or pronunciation as "not quite right" or "almost there," rather than dismissing it as completely unacceptable ...
Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
Treebanks are necessarily constructed according to a particular grammar. The same grammar may be implemented by different file formats. For example, the syntactic analysis for John loves Mary, shown in the figure on the right/above, may be represented by simple labelled brackets in a text file, like this (following the Penn Treebank notation):