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

Authors: Xiyue Guo; Tingting He; Ying Xing

Addresses: National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China; School of Information Technology, Xingyi Normal University for Nationalities, Xingyi, China ' School of Computer, Central China Normal University, Wuhan, China ' Software College, Zhongyuan University of Technology, Zhengzhou, China

Abstract: 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.

Keywords: PPI extraction; protein-protein interactions; weakly supervised; word dictionary construction; rule learning; relational words; relational rules; biomedical literature; bioinformatics; slot filling.

DOI: 10.1504/IJDMB.2016.076533

International Journal of Data Mining and Bioinformatics, 2016 Vol.15 No.2, pp.125 - 144

Received: 09 Feb 2016
Accepted: 09 Feb 2016

Published online: 11 May 2016 *

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