Qu, LizhenFERRARO, GABRIELAZhou, LiyuanHou, WeiweiBaldwin, TimothyMartha PalmerRebecca HwaSebastian Riedel2024-06-212024-06-21September978-1-945626-83-8https://hdl.handle.net/1885/733713321In 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.application/pdfen-AU© 2016 Association for Computational Linguistics201610.18653/v1/d16-10872024-02-18