Active Learning for Coreference Resolution

Florian Laws,  Florian Heimerl,  Hinrich Schütze
Universität Stuttgart


Abstract

We present an active learning method for coreference resolution that is novel in three respects. (i) It uses bootstrapped neighborhood pooling, which ensures a class-balanced pool even though gold labels are not available. (ii) It employs neighborhood selection, a selection strategy that ensures coverage of both positive and negative links for selected markables. (iii) It is based on a query-by-committee selection strategy in contrast to earlier uncertainty sampling work. Experiments show that this new method outperforms random sampling in terms of both annotation effort and peak performance.