The motivations behind our approach are that the unlabeled data can be partitioned into a number of strata using a clustering algorithm and that representative and diverse seeds can be derived from such strata in the framework of stratified sampling (Neyman, 1934) for an oracle to annotate.
we employ a diversity-motivated intra-stratum sampling scheme to pick a center and additional instances as seeds from each stratum.