Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
11-2016
Abstract
In this paper, we study cross-domain sentiment classification with neural network architectures. We borrow the idea from Structural Correspondence Learning and use two auxiliary tasks to help induce a sentence embedding that supposedly works well across domains for sentiment classification. We also propose to jointly learn this sentence embedding together with the sentiment classifier itself. Experiment results demonstrate that our proposed joint model outperforms several state-of-the-art methods on five benchmark datasets.
Keywords
Benchmark datasets, Cross-domain, Joint modeling, Sentiment classification, State-of-the-art methods
Discipline
Databases and Information Systems | Software Engineering
Research Areas
Data Science and Engineering
Publication
EMNLP 2016: Proceedings of the Conference on Empirical Methods in Natural Language Processing: Austin, Texas, November 1-5
First Page
236
Last Page
246
ISBN
9781945626258
Publisher
Association for Computational Linguistics
City or Country
Stroudsburg, PA
Citation
YU, Jianfei and JIANG, Jing.
Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classification. (2016). EMNLP 2016: Proceedings of the Conference on Empirical Methods in Natural Language Processing: Austin, Texas, November 1-5. 236-246.
Available at: https://ink.library.smu.edu.sg/sis_research/3437
Copyright Owner and License
Authors
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/D16-1023/