Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network
Table 9: Contribution of each feature in Joint AB-LSTM model. Here P refers random position embedding for both P1 and P2, and X refers pre-trained word vector embedding
['[ITALIC]  [BOLD] Models', '[ITALIC]  [BOLD] Precision', '[ITALIC]  [BOLD] Recall', '[ITALIC]  [BOLD] F Score']
In order to validate the importance of each feature, we further analyzed the performance of Joint AB-LSTM model by removing feature types one by one. About 1.1% of relative decrement is observed if the model does not use position embedding. On the other hand, removal of position embedding as well as use of random vectors in place of pre-trained word vectors lead to 4.6% of relative decrements in the model’s performance. This analysis clearly indicates the importance of word and position embedding features.