In this paper, we propose a multi-criteriabased active learning approach and effectively apply it to named entity recognition. Active learning targets to minimize the human annotation efforts by selecting examples for labeling. To maximize the contribution of the selected e xamples, we consider the multiple criteria: informativeness, representativeness and diversity and propose measures to quantify them. More comprehensively, we incorporate all the criteria using two selection strategies, both of which result in less labeling cost than single -criterion-based method. The results of the named entity recognition in both MUC-6 and GENIA show that the labeling cost can be reduced by at least 80% without degrading the performance.
REF proposed a multi-criteria-based active learning approach and applied it to named entity recognition.
Multi-Criteria-Based Active Learning For Named Entity Recognition
Annual Meeting Of The Association For Computational Linguistics
Computer Science