This paper proposes the
<term>
Hierarchical Directed Acyclic Graph ( HDAG ) Kernel
</term>
for
<term>
structured natural language data
</term>
.
#3794This paper proposes the Hierarchical Directed Acyclic Graph ( HDAG ) Kernel for structured natural language data.
other,13-1-P03-1005,ak
This paper proposes the
<term>
Hierarchical Directed Acyclic Graph ( HDAG ) Kernel
</term>
for
<term>
structured natural language data
</term>
.
#3803This paper proposes the Hierarchical Directed Acyclic Graph (HDAG) Kernel for structured natural language data.
tech,1-2-P03-1005,ak
The
<term>
HDAG Kernel
</term>
directly accepts several levels of both
<term>
chunks
</term>
and their
<term>
relations
</term>
, and then efficiently computes the
<term>
weighed sum
</term>
of the number of common
<term>
attribute sequences
</term>
of the
<term>
HDAGs
</term>
.
#3809The HDAG Kernel directly accepts several levels of both chunks and their relations, and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs.
other,9-2-P03-1005,ak
The
<term>
HDAG Kernel
</term>
directly accepts several levels of both
<term>
chunks
</term>
and their
<term>
relations
</term>
, and then efficiently computes the
<term>
weighed sum
</term>
of the number of common
<term>
attribute sequences
</term>
of the
<term>
HDAGs
</term>
.
#3817The HDAG Kernel directly accepts several levels of both chunks and their relations, and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs.
other,12-2-P03-1005,ak
The
<term>
HDAG Kernel
</term>
directly accepts several levels of both
<term>
chunks
</term>
and their
<term>
relations
</term>
, and then efficiently computes the
<term>
weighed sum
</term>
of the number of common
<term>
attribute sequences
</term>
of the
<term>
HDAGs
</term>
.
#3820The HDAG Kernel directly accepts several levels of both chunks and their relations , and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs.
measure(ment),19-2-P03-1005,ak
The
<term>
HDAG Kernel
</term>
directly accepts several levels of both
<term>
chunks
</term>
and their
<term>
relations
</term>
, and then efficiently computes the
<term>
weighed sum
</term>
of the number of common
<term>
attribute sequences
</term>
of the
<term>
HDAGs
</term>
.
#3827The HDAG Kernel directly accepts several levels of both chunks and their relations, and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs.
other,26-2-P03-1005,ak
The
<term>
HDAG Kernel
</term>
directly accepts several levels of both
<term>
chunks
</term>
and their
<term>
relations
</term>
, and then efficiently computes the
<term>
weighed sum
</term>
of the number of common
<term>
attribute sequences
</term>
of the
<term>
HDAGs
</term>
.
#3834The HDAG Kernel directly accepts several levels of both chunks and their relations, and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs.
tech,30-2-P03-1005,ak
The
<term>
HDAG Kernel
</term>
directly accepts several levels of both
<term>
chunks
</term>
and their
<term>
relations
</term>
, and then efficiently computes the
<term>
weighed sum
</term>
of the number of common
<term>
attribute sequences
</term>
of the
<term>
HDAGs
</term>
.
#3838The HDAG Kernel directly accepts several levels of both chunks and their relations, and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs .
tech,6-3-P03-1005,ak
We applied the proposed method to
<term>
question classification
</term>
and
<term>
sentence alignment tasks
</term>
to evaluate its performance as a
<term>
similarity measure
</term>
and a
<term>
kernel function
</term>
.
#3846We applied the proposed method to question classification and sentence alignment tasks to evaluate its performance as a similarity measure and a kernel function.
tech,9-3-P03-1005,ak
We applied the proposed method to
<term>
question classification
</term>
and
<term>
sentence alignment tasks
</term>
to evaluate its performance as a
<term>
similarity measure
</term>
and a
<term>
kernel function
</term>
.
#3849We applied the proposed method to question classification and sentence alignment tasks to evaluate its performance as a similarity measure and a kernel function.
measure(ment),18-3-P03-1005,ak
We applied the proposed method to
<term>
question classification
</term>
and
<term>
sentence alignment tasks
</term>
to evaluate its performance as a
<term>
similarity measure
</term>
and a
<term>
kernel function
</term>
.
#3858We applied the proposed method to question classification and sentence alignment tasks to evaluate its performance as a similarity measure and a kernel function.
tech,22-3-P03-1005,ak
We applied the proposed method to
<term>
question classification
</term>
and
<term>
sentence alignment tasks
</term>
to evaluate its performance as a
<term>
similarity measure
</term>
and a
<term>
kernel function
</term>
.
#3862We applied the proposed method to question classification and sentence alignment tasks to evaluate its performance as a similarity measure and a kernel function.
tech,8-4-P03-1005,ak
The results of the experiments demonstrate that the
<term>
HDAG Kernel
</term>
is superior to other
<term>
kernel functions
</term>
and
<term>
baseline methods
</term>
.
#3873The results of the experiments demonstrate that the HDAG Kernel is superior to other kernel functions and baseline methods.
tech,14-4-P03-1005,ak
The results of the experiments demonstrate that the
<term>
HDAG Kernel
</term>
is superior to other
<term>
kernel functions
</term>
and
<term>
baseline methods
</term>
.
#3879The results of the experiments demonstrate that the HDAG Kernel is superior to other kernel functions and baseline methods.
tech,17-4-P03-1005,ak
The results of the experiments demonstrate that the
<term>
HDAG Kernel
</term>
is superior to other
<term>
kernel functions
</term>
and
<term>
baseline methods
</term>
.
#3882The results of the experiments demonstrate that the HDAG Kernel is superior to other kernel functions and baseline methods.