Relation dictionary construction and rule learning for PPI extraction from biomedical literatures

Abstract

Using rules to extract protein-protein interactions (PPI) from biomedical literatures has shown recognized positive effect, but the process of making rules is time-costing and expensive. Relation dictionary-based rule is an effective way to solve the problem, while it also encounters a new problem: how to design an excellent dictionary fast and correctly. This paper proposes a weakly supervised method to construct the PPI relation dictionary, and presents a slot-filling method to learn PPI relation rules automatically according to the position of proteins and relation words. Moreover, this method does not depend on much more manual intervention. We conduct the experiment using 5 types of authoritative biomedical PPI corpus, and the results show that our method can improve the PPI extraction effect obviously.