A Hybrid approach for biomedical relation extraction using finite state automata and random forest-weighted fusion

Abstract

The automatic extraction of relations between medical entities found in related texts is considered to be a very important task, due to the multitude of applications that it can support, from question answering systems to the devel-opment of medical ontologies. Many different methodologies have been pre-sented and applied to this task over the years. Of particular interest are hybrid approaches, in which different techniques are combined in order to improve the individual performance of either one of them. In this study, we extend a previ-ously established hybrid framework for medical relation extraction, which we modify by enhancing the pattern-based part of the framework and by applying a more sophisticated weighting method. Most notably, we replace the use of regu-lar expressions with finite state automata for the pattern-building part, while the fusion part is replaced by a weighting strategy that is based on the operational capabilities of the Random Forests algorithm. The experimental results indicate the superiority of the proposed approach against the aforementioned well-established hybrid methodology and other state-of-the-art approaches.