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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