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.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2013
- DOI:
- 10.48550/arXiv.1302.4874
- arXiv:
- arXiv:1302.4874
- Bibcode:
- 2013arXiv1302.4874S
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Machine Learning