Joint Models for Extracting Adverse Drug Events from Biomedical Text

By Fei Li, Meishan Zhang, Donghong Ji. 2016

60 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Fei Li
2. Meishan Zhang
3. Guohong Fu
4. Donghong Ji
5. Markus Eberts
6. Giannis Bekoulis

Citations in the corpus

• A neural joint model for entity and relation extraction from biomedical text (2017, methodology): “Li et al. [29] exploited a transition-based feed-forward neural network to jointly extract drug-disease entity mentions and their ADE relations.”
• Span-based Joint Entity and Relation Extraction with Transformer Pre-training (2019, unknown): “yGIE [21]y - - 65:20 - - 41:60 DyGIE++ [34]y - -67 :50 48 40 SpERTy(using BERT) 68:53 66:73 67:62 49:79 43:53 46:44 SpERTy(using SciBERT) 70:87 69:79 70:33 53:40 48:54 50:84 ADE CNN + Global features [17]z 79:50 79:60 79:50 64:00 62:90 63:40 BiLSTM + SDP [16]z 82:70 86:70 84:60 67:50 75:80 71:40 Mul”