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Table 2 Performance comparison of Semi-Supervised bi-LSTM (SS-BLSTM) under different word embedding initialization settings and different unlabeled data settings. Results are reported averaged over 30 trials along with the std. deviation

From: Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction

Method F1-Score Precision Recall
SS-BLSTM (with drug mask removed) 0.747 ±0.037 0.723 ±0.106 0.780 ±0.108
SS-BLSTM (with labeled tweets dictionary only) 0.745 ±0.039 0.727 ±0.072 0.769 ±0.097
SS-BLSTM (with GoogleNews [25] vectors) 0.736 ±0.031 0.708 ±0.095 0.774 ±0.118
SS-BLSTM (with medical embeddings) 0.673 ±0.021 0.642 ±0.089 0.716 ±0.118
  1. Highlighted portions reflect the best results across the respective columns