Publication Type

Conference Proceeding Article

Publication Date

2-2017

Abstract

Opinion target extractionis a fundamental task in opinion mining. In recent years,neural network based supervised learning methods haveachieved competitive performance on this task. However, aswith any supervised learning method, neural network basedmethods for this task cannot work well when the training datacomes from a different domain than the test data. On the otherhand, some rule-based unsupervisedmethods have shown to berobust when applied to different domains. In this work, weuse rule-based unsupervised methods to create auxiliary labelsand use neural network models to learn a hiddenrepresentation that works well for different domains. When this hiddenrepresentation is used for opinion target extraction, we findthat it can outperform a number of strong baselines with alarge margin.

Discipline

OS and Networks

Research Areas

Information Systems and Management

Publication

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

Publisher

Association for the Advancement of Artificial Intelligence

City or Country

San Francisco, USA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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