Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2017

Abstract

Opinion target extraction is a fundamental task in opinion mining. In recent years, neural network based supervised learning methods have achieved competitive performance on this task. However, as with any supervised learning method, neural network based methods for this task cannot work well when the training data comes from a different domain than the test data. On the other hand, some rule-based unsupervised methods have shown to be robust when applied to different domains. In this work, we use rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for different domains. When this hidden representation is used for opinion target extraction, we find that it can outperform a number of strong baselines with a large margin.

Keywords

Artificial intelligence, Extraction, Learning systems, Supervised learning

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the 31st AAAI Conference on Artificial Intelligence: San Francisco, CA, 2017 February 4-9

First Page

3436

Last Page

3442

Publisher

AAAI Press

City or Country

Menlo Park, CA

Copyright Owner and License

Publisher

Additional URL

https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14865

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