Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2015
Abstract
We present a simple yet effective unsupervised domain adaptation method that can be generally applied for different NLP tasks. Our method uses unlabeled target domain instances to induce a set of instance similarity features. These features are then combined with the original features to represent labeled source domain instances. Using three NLP tasks, we show that our method consistently out-performs a few baselines, including SCL, an existing general unsupervised domain adaptation method widely used in NLP. More importantly, our method is very easy to implement and incurs much less computational cost than SCL.
Keywords
Computational linguistics, Linguistics, Domain adaptation, Computational costs, Target domain, Natural language processing systems
Discipline
Computer Sciences | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: Beijing, China, July 26-31, 2015
Volume
2
First Page
168
Last Page
173
ISBN
9781941643730
Identifier
10.3115/v1/P15-2028
Publisher
Association for Computational Linguistics
City or Country
Stroudsburg, PA
Citation
YU, Jianfei and Jing JIANG.
A Hassle-free Unsupervised Domain Adaptation Method using Instance Similarity Features. (2015). Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: Beijing, China, July 26-31, 2015. 2, 168-173.
Available at: https://ink.library.smu.edu.sg/sis_research/2981
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.3115/v1/P15-2028