Paper: Hierarchical Directed Acyclic Graph Kernel: Methods For Structured Natural Language Data

ACL ID P03-1005
Title Hierarchical Directed Acyclic Graph Kernel: Methods For Structured Natural Language Data
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2003
Authors

This paper proposes the “Hierarchical Di- rected Acyclic Graph (HDAG) Kernel” for structured natural language data. The HDAG Kernel directly accepts several lev- els of both chunks and their relations, and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs. We applied the proposed method to question classifica- tion and sentence alignment tasks to eval- uate its performance as a similarity mea- sure and a kernel function. The results of the experiments demonstrate that the HDAG Kernel is superior to other kernel functions and baseline methods.