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

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

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...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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