Transfer Learning for Entity Recognition of Novel Classes

Juan Diego Rodriguez, Adam Caldwell, Alexander Liu


Abstract
In this reproduction paper, we replicate and extend several past studies on transfer learning for entity recognition. In particular, we are interested in entity recognition problems where the class labels in the source and target domains are different. Our work is the first direct comparison of these previously published approaches in this problem setting. In addition, we perform experiments on seven new source/target corpus pairs, nearly doubling the total number of corpus pairs that have been studied in all past work combined. Our results empirically demonstrate when each of the published approaches tends to do well. In particular, simpler approaches often work best when there is very little labeled target data, while neural transfer approaches tend to do better when there is more labeled target data.
Anthology ID:
C18-1168
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1974–1985
Language:
URL:
https://aclanthology.org/C18-1168
DOI:
Bibkey:
Cite (ACL):
Juan Diego Rodriguez, Adam Caldwell, and Alexander Liu. 2018. Transfer Learning for Entity Recognition of Novel Classes. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1974–1985, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Transfer Learning for Entity Recognition of Novel Classes (Rodriguez et al., COLING 2018)
Copy Citation:
PDF:
https://aclanthology.org/C18-1168.pdf
Code
 ciads-ut/transfer-learning-ner