On the Effectiveness of the Pooling Methods for Biomedical Relation Extraction with Deep Learning

Tuan Ngo Nguyen, Franck Dernoncourt, Thien Huu Nguyen


Abstract
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.
Anthology ID:
D19-6203
Volume:
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | Louhi | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
18–27
Language:
URL:
https://www.aclweb.org/anthology/D19-6203
DOI:
10.18653/v1/D19-6203
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/D19-6203.pdf