Extracting Chemical-Protein Interactions via Calibrated Deep Neural Network and Self-training

Abstract

The extraction of interactions between chemicals and proteins from several biomedical articles is important in many fields of biomedical research such as drug development and prediction of drug side effects. Several natural language processing methods, including deep neural network (DNN) models, have been applied to address this problem. However, these methods were trained with hard-labeled data, which tend to become over-confident, leading to degradation of the model reliability. To estimate the data uncertainty and improve the reliability, "calibration" techniques have been applied to deep learning models. In this study, to extract chemical-protein interactions, we propose a DNN-based approach incorporating uncertainty information and calibration techniques. Our model first encodes the input sequence using a pre-trained languageunderstanding model, following which it is trained using two calibration methods: mixup training and addition of a confidence penalty loss. Finally, the model is retrained with augmented data that are extracted using the estimated uncertainties. Our approach has achieved state-of-the-art performance with regard to the Biocreative VI ChemProt task, while preserving higher calibration abilities than those of previous approaches. Furthermore, our approach also presents the possibilities of using uncertainty estimation for performance improvement.

Re-Ranking System with BERT for Biomedical Concept Normalization

“…where β controls the strength of the confidence penalty. Thus, the incorporation of negative entropy into the original loss function enables overfitting and improves the generalization performance [49]. Similar to previous method [39], we defined the probability of label '1' obtained from the softmax function as a ranking BERT-score (score RB ) of each mention-candidate pair (m, n) as follows:…”