Our current goal is to get the most out of supervised training data by leveraging unannotated data via selectors (no use of a knowledge-base or similarity metrics).
our system achieves state-of-the-art results in line with top supervised systems while our earlier knowledge-based approaches produce results in line with systems not utilizing training data.