| Predication {LSAfun} | R Documentation | 
Compute Vector for Predicate-Argument-Expressions
Description
Computes vectors for complex expressions of type PREDICATE[ARGUMENT] by applying the method of Kintsch (2001) (see Details).
Usage
Predication(P,A,m,k,tvectors=tvectors,norm="none")Arguments
| P | Predicate of the expression, a single word (character vector) | 
| A | Argument of the expression, a single word (character vector) | 
| m | number of nearest words to the Predicate that are initially activated | 
| k | size of the  | 
| tvectors | the semantic space in which the computation is to be done (a numeric matrix where every row is a word vector) | 
| norm | whether to  | 
Details
The vector for the expression is computed following the Predication Process by Kintsch (2001):
The m nearest neighbors to the Predicate are computed. Of those, the k nearest neighbors to
the Argument are selected. The vector for the expression is then computed as the sum of
Predicate vector, Argument vector, and the vectors of those k neighbors (the k-neighborhood).
Value
An object of class Pred: This object is a list consisting of:
| $PA | The vector for the complex expression as described above | 
| $P.Pred | The vector for Predicate plus the k-neighborhoodvectors without the Argument vector | 
| $neighbors | The words in the k-neighborhood. | 
| $P | The Predicate given as input | 
| $A | The Argument given as input | 
Author(s)
Fritz Guenther
References
Kintsch, W. (2001). Predication. Cognitive Science, 25, 173-202.
See Also
cosine,
neighbors,
multicos,
compose
Examples
data(wonderland)
Predication(P="mad",A="hatter",m=20,k=3,tvectors=wonderland)