LPTK: a linguistic pattern-aware dependency tree kernel approach for the BioCreative VI CHEMPROT task.

ABSTRACT: Identifying the interactions between chemical compounds and genes from biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this paper, we describe Linguistic Pattern-Aware Dependency Tree Kernel, a linguistic interaction pattern learning method developed for CHEMPROT task-BioCreative VI, to capture chemical-protein interaction (CPI) patterns within biomedical literatures. We also introduce a framework to integrate these linguistic patterns with smooth partial tree kernel to extract the CPIs. This new method of feature representation models aspects of linguistic probability in geometric representation, which not only optimizes the sufficiency of feature dimension for classification, but also defines features as interpretable contexts

SUBMITTER: Warikoo N 

PROVIDER: S-EPMC6196310 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature