By James Allen

ISBN-10: 0805303308

ISBN-13: 9780805303308

From a number one authority in synthetic intelligence, this e-book gives you a synthesis of the main glossy thoughts and the most up-tp-date learn in normal language processing. The procedure is exclusive in its insurance of semantic interpretation and discourse along the foundational fabric in syntactic processing.

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E, on the choice of confidence levels or a-cuts induced by these confidence levels [9]. For instance, one expert assigns the same confidence degree to two possibilistic variables A and B, This suggests a strong dependency between the precision of A and the precision of B. However, this form of dependency does not suppose any objective dependence between possibilistic variables themselves. ,X„ are linked or not. The hybrid method also supposes independence between the group of probabilistic and possibilistic variables.

To underline the way of proportionalization we call this PT as a proportional belief transformation PropBT now. (^)--WIf E B E X ^ ( ^ ) — 0 then |X| is used instead of it, thus m{X) is reallocated per the same portions among all elements of X in such a case. Unfortunately, it is similarly to BeLT significantly sensitive to belief (i. e. to m{X)) of singletons. To improve it the stepwise proportional transformation has been suggested, where m{X) for \X\ — {n-\-l—i), are reallocated in i-th step among m{X) for \X\ = {n — i).

E. variability. As can be observed on some examples, the use of 7cJ may put excessive weight on outlier fuzzy values, in the case of precise results with high variability, even if some outliers have been deleted by the threshold d. The postprocessing proposed by Guyonnet et al. is thus debatable. Better alternative postprocessings can separately treat variability and imprecision. 2. Measuring variability and imprecision separately We may wis;h to define separate indices of variability and imprecision.

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Natural Language Understanding by James Allen

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