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The simplest type of referring expressions are pronoun such as he and it. The linguistics and natural language processing communities have developed various models for predicting anaphor referents, such as centering theory, [1] and ideally referring-expression generation would be based on such models. However most NLG systems use much simpler ...
Definite referring expressions refer to an identifiable individual or class (The Dalai Lama; The Coldstream Guards; the student with the highest marks), whilst indefinite referring expressions allow latitude in identifying the referent (a corrupt Member of Parliament; a cat with black ears—where a is to be interpreted as 'any' or 'some actual ...
The theory classifies nominals according to two features, [±anaphor] and [±pronominal], which are binary. The binding characteristics of a nominal are determined by the values of these features, either plus or minus. Thus, a nominal that is [-anaphor, -pronominal] is an R-expression (referring expression), such as a common noun or a proper name.
In Noam Chomsky's government and binding theory in linguistics, an R-expression (short for "referring expression" (the linked article explains the different, broader usage in other theories of linguistics) or "referential expression") is a noun phrase that refers to a specific real or imaginary entity.
Verbal context influences the way an expression is understood; hence the norm of not citing people out of context. Since much contemporary linguistics takes texts, discourses, or conversations as the object of analysis, the modern study of verbal context takes place in terms of the analysis of discourse structures and their mutual relationships ...
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Ben Affleck and Jennifer Garner are continuing to spend time together this holiday season.. The former couple — who share kids Violet, 19, Seraphina, 15, and Samuel, 12 — were spotted out for ...
In standard autoregressive language modeling, the model would be tasked with predicting the probability of each word, conditioned on the previous words as its context: We factorize the joint probability of a sequence of words x 1 , … , x T {\displaystyle x_{1},\ldots ,x_{T}} using the chain rule: Pr ( x 1 , … , x T ) = Pr ( x 1 ) Pr ( x 2 ...