  The community of different types of microbes present in a biological niche
plays a very important role in functioning of the system. The crosstalk or
interactions among the different microbes contributes to the building blocks of
such microbial community structures. Evidence reported in biomedical text
serves as a reliable source for predicting such interactions. However, going
through the vast and ever-increasing volume of biomedical literature is an
intimidating and time consuming process. This necessitates development of
automated methods capable of accurately extracting bacterial relations reported
in biomedical literature. In this paper, we introduce a method for automated
extraction of microbial interactions (specifically between bacteria) from
biomedical literature along with ways of using transfer learning to improve its
accuracy. We also describe a pipeline using which relations among specific
bacteria groups can be mined. Additionally, we introduce the first publicly
available dataset which can be used to develop bacterial interaction extraction
methods.
