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 l...