Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network
Table 7: Performance comparison between the proposed methods and top-ranking approaches on the test data. Performance is measured based on precision, recall and F1 score. The highest scores are highlighted in bold.
['[BOLD] Method', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F Score']
Comparison based on the filtered dataset: Joint AB-LSTM model obtained the best F1 score of 69.39%. There is 3.6% of relative improvement in F1 score on comparison with the best performing method (Kim2) among feature based linear and kernel methods. In comparison to SCNN2 model, all three proposed models gave similar results for this task. SCNN2, a convolution neural network based model, uses higher order grammatical features based on parts of speech and shortest dependency path apart from pre-trained word embedding and position embedding features. If we remove shortest dependency path features from SCNN2 model, its performance decreases to 63.8% from 68.6%. On the other hand, apart from Joint AB-LSTM, the other two models also gave relatively much better performance than SCNN2 with only using simple features.