Table 1 Deep learning based systems results on the DDI corpus for the DDI classification task (best results in italic)
From: Evaluation of pooling operations in convolutional architectures for drug-drug interaction extraction
Systems | Approach | P | R | F1 |
|---|---|---|---|---|
Sahu and Anand [24] | Combined B-LSTM + AB-LSTM | 73.41% | 69.66% | 71.48% |
Liu et al. [17] | Combined CNN + DCNN | 78.24% | 64.66% | 70.81% |
Sahu and Anand [24] | B-LSTM | 75.97% | 65.57% | 70.39% |
Liu et al. [17] | MCCNN | 75.99% | 65.25% | 70.21% |
Liu et al. [17] | DCNN | 77.21% | 64.35% | 70.19% |
Liu et al. [15] | CNN with MEDLINE word embedding | 75.72% | 64.66% | 69.75% |
Zhao et al. [21] | Two-stage SCNN | 72.5% | 65.1% | 68.6% |
Zhao et al. [21] | One-stage SCNN | 69.1% | 65.1% | 67% |
Sahu and Anand [24] | AB-LSTM | 67.85% | 65.98% | 66.9% |
Suárez-Paniagua et al. [16] | CNN with random word embedding | 69.86% | 56.1% | 62.23% |