Context awareness and embedding for biomedical event extraction
Table 3: Hyper-parameters used in VecEntNet
['VeComNet MLP input units', 'VeComNet 128']
The training set and development set are combined to form an annotated dataset. We evaluate our method under 10-fold cross-validation. For the arguments or events in BioNLPST-BGI with less than 20 data instances, we change to 5-fold cross-validation to ensure that the testing set would not have less than 2 classes. To ensure the training quality of those few labels, we randomly duplicate the samples in the training set so that the binary class ratio is bounded by 5. Only the training samples are duplicated when training the argument embedding. The testing samples are neither duplicated nor used in argument embedding. We trained our models on a Linux machine equipped with a 32-core CPU and 32GB RAM. Parameter analysis is also conducted to show the robustness of our method. The results shown in the Supplementary indicate that our method are not sensitive to the hyper-parameters.