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 theirrelations, and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs.
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 andbaseline methods.
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 forstructured natural language data.
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 akernel function.
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 theHDAGs.
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 asimilarity measure and a kernel function.
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 otherkernel functions and baseline methods.
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 toquestion classification and sentence alignment tasks to evaluate its performance as a similarity measure and a kernel function.
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 bothchunks and their relations, and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs.
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>
.
#3809TheHDAG 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,4-1-P03-1005,ak
This paper proposes the
<term>
Hierarchical Directed Acyclic Graph ( HDAG ) Kernel
</term>
for
<term>
structured natural language data
</term>
.
#3794This paper proposes theHierarchical Directed Acyclic Graph ( HDAG ) Kernel for structured natural language data.
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 theHDAG Kernel is superior to other kernel functions and baseline methods.
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 commonattribute 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 theweighed sum of the number of common attribute sequences of the HDAGs.
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 andsentence alignment tasks to evaluate its performance as a similarity measure and a kernel function.