Paper: Multi-Criteria-Based Active Learning For Named Entity Recognition

ACL ID P04-1075
Title Multi-Criteria-Based Active Learning For Named Entity Recognition
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

In this paper, we propose a multi-criteria - based active learning approach and effec- tively apply it to named entity recognition. Active learning targets to minimize the human annotation efforts by selecting ex- amples for labeling. To maximize the con- tribution of the selected examples, we consider the multiple criteria: informative- ness, 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% with- out degrading the performance.