Description Usage Arguments Details Value Author(s) References See Also Examples
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).
1 2 3 |
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) |
breakdown |
if |
Let p be the vector with entries p_i for the two-word phrase consisiting of u with entries u_i and v with entries v_i. The different composition methods as described by Mitchell & Lapata (2008, 2010) are as follows:
Additive Model (method = "Add"
)
p_i = u_i + v_i
Weighted Additive Model (method = "WeightAdd"
)
p_i = a*u_i + b*v_i
Multiplicative Model (method = "Multiply"
)
p_i = u_i * v_i
Combined Model (method = "Combined"
)
p_i = a*u_i + b*v_i + c*u_i*v_i
Predication (method = "Predication"
)
(see Predication
)
If method="Predication"
is used, x
will be taken as Predicate and y
will be taken as Argument of the phrase (see Examples)
Circular Convolution (method = "CConv"
)
p_i = ∑\limits_{j} u_j * v_{i-j}
,
where the subscripts of v are interpreted modulo n with n = length(x)
(= length(y)
)
Dilation (method = "Dilation"
)
p = (u*u)*v + (λ - 1)*(u*v)*u
,
with (u*u) being the dot product of u and u (and (u*v) being the dot product of u and v).
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")
The phrase vector as a numeric vector
Fritz G?nther
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.
1 2 3 4 5 6 7 8 9 10 11 12 | 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)
|
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