Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models
Table 3: Comparison of performance of our model with baseline models
['[BOLD] Method', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1-Score']
Pyysalo et al. Zhou et al. Wang et al. Apart from the proposed models we also compare our results with two more baseline methods FFNN and CNNψ which are our implementations. Here FFNN is a window based feed forward neural network where embedding features of words within the window are used to predict the trigger label Collobert et al. We chose window size as 3 (one word from left and one word from right) after tuning it in validation set. CNNψ is our implementation of window based CNN classifier proposed by Wang et al. due to unavailability of their code in public domain. Our proposed model have shown slight improvement in F1-score when compared with baseline models. The proposed model’s ability to capture the context of the whole sentence is likely to be one of the reasons of improvement in performance.