On the Effectiveness of the Pooling Methods for Biomedical Relation Extraction with Deep Learning
This work addresses the challenge of selecting optimal pooling methods for biomedical relation extraction, which is incremental as it compares existing strategies.
The study evaluated different pooling mechanisms for deep learning models in biomedical relation extraction, finding that dependency-based pooling achieved state-of-the-art performance on two benchmark datasets.
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.