compose {LSAfun} | R Documentation |
Two-Word Composition
Description
Computes the vector of a complex expression p consisting of two single words u and v, following the methods examined in Mitchell & Lapata (2008) (see Details).
Usage
## Default
compose(x,y,method="Add", a=1,b=1,c=1,m,k,lambda=2,
tvectors=tvectors, norm="none")
Arguments
x |
a single word (character vector with |
y |
a single word (character vector with |
a , b , c |
weighting parameters, see Details |
m |
number of nearest words to the Predicate that are initially activated (see |
k |
size of the |
lambda |
dilation parameter for |
method |
the composition method to be used (see Details) |
norm |
whether to |
tvectors |
the semantic space in which the computation is to be done (a numeric matrix where every row is a word vector) |
Details
Let be the vector with entries
for the two-word phrase consisiting of
with entries
and
with entries
.
The different composition methods as described by Mitchell & Lapata (2008, 2010) are as follows:
Additive Model (
method = "Add"
)Weighted Additive Model (
method = "WeightAdd"
)Multiplicative Model (
method = "Multiply"
)Combined Model (
method = "Combined"
)Predication (
method = "Predication"
) (seePredication
)If
method="Predication"
is used,x
will be taken as Predicate andy
will be taken as Argument of the phrase (see Examples)Circular Convolution (
method = "CConv"
),
where the subscripts of
are interpreted modulo
with
length(x)
(=length(y)
)Dilation (
method = "Dilation"
),
with
being the dot product of
and
(and
being the dot product of
and
).
The Add, Multiply,
and CConv
methods are symmetrical composition methods,
i.e. compose(x="word1",y="word2")
will give the same results as compose(x="word2",y="word1")
On the other hand, WeightAdd, Combined, Predication
and Dilation
are asymmetrical, i.e. compose(x="word1",y="word2")
will give different results than compose(x="word2",y="word1")
Value
The phrase vector as a numeric vector
Author(s)
Fritz Guenther
References
Kintsch, W. (2001). Predication. Cognitive science, 25, 173-202.
Mitchell, J., & Lapata, M. (2008). Vector-based Models of Semantic Composition. In Proceedings of ACL-08: HLT (pp. 236-244). Columbus, Ohio.
Mitchell, J., & Lapata, M. (2010). Composition in Distributional Models of Semantics. Cognitive Science, 34, 1388-1429.
See Also
Examples
data(wonderland)
compose(x="mad",y="hatter",method="Add",tvectors=wonderland)
compose(x="mad",y="hatter",method="Combined",a=1,b=2,c=3,
tvectors=wonderland)
compose(x="mad",y="hatter",method="Predication",m=20,k=3,
tvectors=wonderland)
compose(x="mad",y="hatter",method="Dilation",lambda=3,
tvectors=wonderland)