A Labeled Graph Kernel for Relationship Extraction
Abstract
In this paper, we propose an approach for Relationship Extraction (RE) based on labeled graph kernels. The kernel we propose is a particularization of a random walk kernel that exploits two properties previously studied in the RE literature: (i) the words between the candidate entities or connecting them in a syntactic representation are particularly likely to carry information regarding the relationship; and (ii) combining information from distinct sources in a kernel may help the RE system make better decisions. We performed experiments on a dataset of protein-protein interactions and the results show that our approach obtains effectiveness values that are comparable with the state-of-the art kernel methods. Moreover, our approach is able to outperform the state-of-the-art kernels when combined with other kernel methods.
Cite
@article{arxiv.1302.4874,
title = {A Labeled Graph Kernel for Relationship Extraction},
author = {Gonçalo Simões and Helena Galhardas and David Matos},
journal= {arXiv preprint arXiv:1302.4874},
year = {2013}
}