Recurrent Neural Networks with Auxiliary Labels for Cross-Domain Opinion Target Extraction

This work uses rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for different domains and finds that it can outperform a number of strong baselines with a large margin.

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.