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