Recurrent Neural Networks with Auxiliary Labels for Cross-domain Opinion Target Extraction
Speaker (s): 
DING Ying
PhD Candidate
School of Information Systems
Singapore Management University | Date:
Time:
Venue:
| | January 25, 2017, Wednesday
3:30 pm - 4:30 pm
Meeting Room 4.4, Level 4
School of Information Systems
Singapore Management University
80 Stamford Road
Singapore 178902
We look forward to seeing you at this research seminar. ![]()
|
|
ABOUT THE TALK
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.
This a pre-conference talk for Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17).
About the Speaker
DING Ying is a PhD candidate in School of Information Systems, Singapore Management University, under the supervision of Associate Professor JIANG Jing. He works in the area of text mining and recommender systems. His primary research interest focuses on using advanced techniques in natural language processing and machine learning to improve the performance of existing recommender systems. He has also done some exploration in text summarization, topic modelling and social media mining.
