Named Entity Recognition for Novel Types by Transfer Learning
Lizhen Qu, Gabriela Ferraro, Liyuan Zhou, Weiwei Hou, Timothy J. Baldwin · 2016
In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly.In this paper, we propose a method where, given training data in a related domain with similar (but not identical) named entity (NE) types and a small amount of in-domain training data, we use transfer learning to learn a domain-specific NE model.That is, the novelty in the task setup is that we assume not just domain mismatch, but also label mismatch.