Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network
Table 6: Performance comparison of proposed models with existing models on complete dataset, i.e. without using negative instance filtering, for DDI classification task.
['[ITALIC]  [BOLD] Models', '[ITALIC]  [BOLD] Precision', '[ITALIC]  [BOLD] Recall', '[ITALIC]  [BOLD] F1 Score']
Comparison based on the complete dataset: First we compare the three proposed models with other existing models on the complete dataset, i.e., without using the filtering step. Here, we have included only those methods which explicitly mention about the similar steps and report corresponding results. B-LSTM and Joint AB-LSTM models have outperformed all other models. We performed t-test (one-sided null hypothesis: mean performance of model 1 is not less than that of model 2) on mean performance of model pairs. Mean performance of a model was calculated as the average of F1 score obtained in five different runs of the model. B-LSTM and Joint AB-LSTM models outperformed CNN*1 model at the significance level of 0.05. In particular p-values of CNN*1 vs B-LSTM, AB-LSTM, and Joint AB-LSTM comparisons were 0.0007,0.04,0.01 respectively. Among the LSTM models, no single model outperforms other models at the above chosen significance level.