Towards Discourse Parsing-inspiredSemantic Storytelling
Table 1: Results of training multi-class prediction based on PDTB2 data set in a 80-20 train-test-split.
['[BOLD] PDTB Relation', '[BOLD]  Precision', '[BOLD]  Recall', '[BOLD]  F1-score', '[BOLD] Support']
For evaluation, we use the multi class metric F1-micro average, which calculates the metrics globally by counting the total true positives, false negatives and false positives to compute the average metric. In a multi-class classification setup, micro-average is preferable if you suspect there might be class imbalance. In the end, we achieve 0.55 micro average F1. Due to the fact that we have not implemented features relating to the connective, our classification performs lower than current state-of-the-art approaches.