Distributed Tree Kernels
This addresses efficiency issues for researchers and practitioners using tree kernels in natural language processing, though it appears incremental as it builds on existing tree kernel methods.
The authors tackled the computational complexity of tree kernels by proposing distributed tree kernels (DTK), which embed tree fragments in low-dimensional spaces using a linear complexity algorithm, resulting in faster computation while maintaining statistically similar performance in two NLP tasks.
In this paper, we propose the distributed tree kernels (DTK) as a novel method to reduce time and space complexity of tree kernels. Using a linear complexity algorithm to compute vectors for trees, we embed feature spaces of tree fragments in low-dimensional spaces where the kernel computation is directly done with dot product. We show that DTKs are faster, correlate with tree kernels, and obtain a statistically similar performance in two natural language processing tasks.