Named entity recognition using point prediction and active learning

Named entity recognition (NER) research has been spreading into specialty domains. A specialty domain corpus is smaller than a general domain corpus. Moreover, annotating a specialty domain corpus is more expensive than annotating a general corpus. Therefore, in this paper, we introduce a model that uses point-wise prediction and active learning to achieve a high extraction performance even in a small annotation corpus. We demonstrate the effectiveness of our approach through a simulation of active learning.