That is, the key to his program is that for every word in the dictionary and every sentence in the language there corresponds a unique predicate that is true of just the tokens of that word/that sentence.
In rare cases where there is only one unique predicate, PredScal defaults to a value of 1.
The Kullback-Leibler Divergence (KLD) determines the difference between a true distribution (P) and an assumed distribution (Q): D (P | | Q ) = ∑ P (x ) log 2 (P (x ) / Q (x ) ) where x represents the relative frequency of each unique predicate in each distribution.
Therefore, we can conclude that this feature is a salient role to the data set P. Consider (1) D P || Q = ∑ P x log 2 P x Q x, where x represents the relative frequency of each unique predicate in each distribution.
Therefore, we created a scaling function named PredScal to scale RlogF values according to the spatial proportions of predicates in a given dataset: PredScal = 1 / log 2 (c ) In this metric, c represents the count of unique predicates in a dataset.
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For example, if there were 16 unique predicates in a dataset, PredScal would equal a scaling factor of 0.25.
Raw RlogF scores often exceed KLD scores, so we created a new metric called PredScal to scale and smooth RlogF scores according to the spatial proportions of predicates in a given SemRep dataset: (4) 1 / log 2 c Here, c represents the count of unique predicates.
One might imagine that both new substances would have all of x's pre-division predicates in common and unique predicates thereafter.
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