  Medical relation extraction discovers relations between entity mentions in
text, such as research articles. For this task, dependency syntax has been
recognized as a crucial source of features. Yet in the medical domain, 1-best
parse trees suffer from relatively low accuracies, diminishing their
usefulness. We investigate a method to alleviate this problem by utilizing
dependency forests. Forests contain many possible decisions and therefore have
higher recall but more noise compared with 1-best outputs. A graph neural
network is used to represent the forests, automatically distinguishing the
useful syntactic information from parsing noise. Results on two biomedical
benchmarks show that our method outperforms the standard tree-based methods,
giving the state-of-the-art results in the literature.
