This before That:Causal Precedence in the Biomedical Domain
Table 4: Results of all proposed causal models, using stratified 10-fold cross-validation. The combined system is a sieve-based architecture that applies the models in decreasing order of their precision. The combined system significantly outperforms the best single model, SVM with L1 regularization, according to a bootstrap resampling test (p = 0.022).
['[ITALIC] Model', '[ITALIC] p', '[ITALIC] r', '[ITALIC] f1']
We report results using micro precision, recall, and F1 scores for each model. With fewer than 200 instances of causal precedence occurring in 1000 annotations, training and testing for both the feature-based classifiers and latent feature models was performed using stratified 10-fold cross validation. Weight updates were made on batches of 32 examples and all folds completed in fewer than 50 epochs.