Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification

Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier


Abstract
We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our approach explicitly minimizes the distance between the source and the target instances in an embedded feature space. With the difference between source and target minimized, we then exploit additional information from the target domain by consolidating the idea of semi-supervised learning, for which, we jointly employ two regularizations — entropy minimization and self-ensemble bootstrapping — to incorporate the unlabeled target data for classifier refinement. Our experimental results demonstrate that the proposed approach can better leverage unlabeled data from the target domain and achieve substantial improvements over baseline methods in various experimental settings.
Anthology ID:
D18-1383
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3467–3476
Language:
URL:
https://aclanthology.org/D18-1383
DOI:
10.18653/v1/D18-1383
Bibkey:
Cite (ACL):
Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2018. Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3467–3476, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification (He et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1383.pdf
Attachment:
 D18-1383.Attachment.pdf
Code
 ruidan/DAS