Kernel methods are considered the most effective techniques for various relation extraction (RE) tasks as they provide higher accuracy than other approaches. In this paper, we introduce new dependency tree (DT) kernels for RE by improving on previously proposed dependency tree structures. These are further enhanced to design more effective approaches that we call mildly extended dependency tree (MEDT) kernels. The empirical results on the protein-protein interaction (PPI) extraction task on the AIMed corpus show that tree kernels based on our proposed DT structures achieve higher accuracy than previously proposed DT and phrase structure tree (PST) kernels.
A Study on Dependency Tree Kernels for Automatic Extraction of Protein-Protein Interaction
Chowdhury, Faisal Mahbub;Lavelli, Alberto;
2011-01-01
Abstract
Kernel methods are considered the most effective techniques for various relation extraction (RE) tasks as they provide higher accuracy than other approaches. In this paper, we introduce new dependency tree (DT) kernels for RE by improving on previously proposed dependency tree structures. These are further enhanced to design more effective approaches that we call mildly extended dependency tree (MEDT) kernels. The empirical results on the protein-protein interaction (PPI) extraction task on the AIMed corpus show that tree kernels based on our proposed DT structures achieve higher accuracy than previously proposed DT and phrase structure tree (PST) kernels.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
