Entity-pair embeddings for improving relation extraction in the biomedical domain - Mehryary F.; Moen H.; Salakoski T.; Ginter F. (Belgium, 2022)
We introduce a new approach for training named-entity pair embeddings to improve relation extraction performance in the biomedical domain. These embeddings are trained in an unsupervised manner, based on the principles of distributional semantics. By adding them to neural network architectures, we show that improved F-Scores are achieved. Our best performing neural model which utilizes entity-pair embeddings along with a pre-trained BERT encoder, achieves an F-score of 77.19 on CHEMPROT (Chemical-Protein) relation extraction corpus, setting a new state-of-the-art result for the task.
ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning