To generate a ranking of U R using the learned HMM parameters, we rank the arguments e i according to how similar their state distributions P (t|e i ) are to those of the seed arguments.
3 we define a function: where KL represents KL divergence, and the outer sum is taken over the arguments e i of the extraction e. We rank the elements of U R in ascending order of f (e).