Microbial Interaction Extraction from Biomedical Literature using Max-Bi-LSTM

Microorganisms play a vital role in various ecosystems, but their complex interaction is still unclear. With the publication of a large number of microbial literatures, many experimentally verified microbial interaction is dispersed therein. Organizing them into a database or knowledge graph can facilitate the development of microbiology research. Text mining technology is able to automatically extract and integrate these microbial interactions, as well as discover implicit information in literatures. For this purpose, we manually annotate a Microbial Interaction Corpus (MICorpus) containing 1005 abstracts, which provide a useful data source for the MIE task. On this basis, we propose an automated MIE extraction system based on Max-Bi-LSTM model. The best result of the system is precision (P) 76.313%, recall (R) of 90.121%, and an F value (F) 82.476%.