A Hassle-free Unsupervised Domain Adaptation Method using Instance Similarity Features

• Main Authors: YU, Jianfei, Jing JIANG
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Computational linguistics; Linguistics; Domain adaptation; Computational costs; Target domain; Natural language processing systems; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2981; https://ink.library.smu.edu.sg/context/sis_research/article/3981/viewcontent/P15_2028.pdf
• Institution: Singapore Management University

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.