@InProceedings{song-etal-2019-leveraging, author = {Linfeng Song and Yue Zhang and Daniel Gildea and Mo Yu and Zhiguo Wang and Jinsong Su}, title = {Leveraging Dependency Forest for Neural Medical Relation Extraction}, booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, year = {2019}, month = {November}, pages = {208--218}, address = {Hong Kong}, URL = {https://www.aclweb.org/anthology/D19-1020.pdf}, abstract = {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 more than one 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 benchmarks show that our method outperforms the standard tree-based methods, giving the state-of-the-art results in the literature.} }