Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2012
Abstract
We use multiple views for cross-domain document classification. The main idea is to strengthen the views’ consistency for target data with source training data by identifying the correlations of domain-specific features from different domains. We present an Information-theoretic Multi-view Adaptation Model (IMAM) based on a multi-way clustering scheme, where word and link clusters can draw together seemingly unrelated domain-specific features from both sides and iteratively boost the consistency between document clusterings based on word and link views. Experiments show that IMAM significantly outperforms state-of-the-art baselines.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012)
First Page
270
Last Page
274
Publisher
Association for Computational Linguistics
City or Country
Jeju Island, Korea
Citation
YANG, Pei; GAO, Wei; TAN, Qi; and WONG, Kam-Fai.
Information-theoretic multi-view domain adaptation. (2012). Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012). 270-274.
Available at: https://ink.library.smu.edu.sg/sis_research/4590
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://www.aclweb.org/anthology/P12-2053/