TY - GEN
T1 - Named entity recognition for novel types by transfer learning
AU - Qu, Lizhen
AU - Ferraro, Gabriela
AU - Zhou, Liyuan
AU - Hou, Weiwei
AU - Baldwin, Timothy
N1 - Publisher Copyright:
© 2016 Association for Computational Linguistics
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85021835278&partnerID=8YFLogxK
U2 - 10.18653/v1/d16-1087
DO - 10.18653/v1/d16-1087
M3 - Conference contribution
T3 - EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings
SP - 899
EP - 905
BT - EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PB - Association for Computational Linguistics (ACL)
T2 - 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016
Y2 - 1 November 2016 through 5 November 2016
ER -