  Deep learning models have achieved state-of-the-art performances on many
relation extraction datasets. A common element in these deep learning models
involves the pooling mechanisms where a sequence of hidden vectors is
aggregated to generate a single representation vector, serving as the features
to perform prediction for RE. Unfortunately, the models in the literature tend
to employ different strategies to perform pooling for RE, leading to the
challenge to determine the best pooling mechanism for this problem, especially
in the biomedical domain. In order to answer this question, in this work, we
conduct a comprehensive study to evaluate the effectiveness of different
pooling mechanisms for the deep learning models in biomedical RE. The
experimental results suggest that dependency-based pooling is the best pooling
strategy for RE in the biomedical domain, yielding the state-of-the-art
performance on two benchmark datasets for this problem.
