Construction of relational word dictionary and learning of relational rules in PPI extraction from biomedical literatures

摘要

Each method, machine learning-based and rule-based, for extracting PPI (Protein-Protein Interactions) from biomedical literatures has advantages and disadvantages. In order to utilise the superiorities of these methods reasonably, this paper designs a new structure for the relational word dictionary, uses weakly supervised method to find dictionary items and fill them into the PPI relational word dictionary, and presents a method to learn PPI relational rules automatically based on slot-filling principle. Moreover, this method takes the PPI relation instances without apparent relational words into consideration aiming to improve the final performance. We conduct the experiments with five authoritative biomedical PPI corpuses, and discover some distribution features about PPI relational words. Finally, we also compare our method with several recent research achievements, and the results show that the performance of our method is better than the average level among these methods.