Named Entity Recognition for Novel Types by Transfer Learning
Computation and Language
2016-11-01 v1
Abstract
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.
Cite
@article{arxiv.1610.09914,
title = {Named Entity Recognition for Novel Types by Transfer Learning},
author = {Lizhen Qu and Gabriela Ferraro and Liyuan Zhou and Weiwei Hou and Timothy Baldwin},
journal= {arXiv preprint arXiv:1610.09914},
year = {2016}
}