Feature Assisted bi-directional LSTM Model for Protein-Protein Interaction Identification from Biomedical Texts
Table 6: Comparative results of the proposed model (sdpLSTM) with different baselines and state-of-the-art systems. Ref. choi2016extraction ∗ and li2015approach ∗ denote the reimplementation of the systems proposed in choi2016extraction and li2015approach with the authors reported experimental setups.
['[BOLD] Model', '[BOLD] Approach', '[BOLD] AIMED  [BOLD] Precision', '[BOLD] AIMED  [BOLD] Recall', '[BOLD] AIMED  [BOLD] F1-Score', '[BOLD] BioInfer  [BOLD] Precision', '[BOLD] BioInfer  [BOLD] Recall', '[BOLD] BioInfer  [BOLD] F1-Score']
We perform 10-fold cross validation on both the datasets. With no official development data set available, cross validation seems to be the most reliable method of evaluating our proposed model. To evaluate the performance of our model, we use standard recall, precision, and F1-score. The obtained results clearly show the effectiveness of our proposed sdpLSTM based model over the other models exploring neural network architectures or conventional kernel or SDP based machine learning model. Statistical significance tests verify that improvements over both the baselines are statistically significant as (p-value < 0.05). In our proposed model we obtain the significant F1-score improvement of 19.99 and 16.23 points over the first two baselines for the AiMed dataset, respectively. On BioInfer dataset, our system shows the F1-Score improvement of 7.13 and 4.14 points over these two baselines, respectively. In order to perform the comparative analysis with the existing approaches, we choose the recent approach exploiting neural network model. We observe that sdpLSTM significantly performs better than all the state-of the-art techniques for both the datasets. We further make an interesting observation that incorporating the latent features embedded into the neural network based architecture improves the performance of the system.