Learning Explicit and Implicit Structures for Targeted Sentiment Analysis
This work addresses the need for improved accuracy in targeted sentiment analysis, which is incremental as it builds on prior sequence labeling methods.
The paper tackled the problem of targeted sentiment analysis by proposing a model that captures both explicit and implicit structural information, achieving better performance than existing approaches.
Targeted sentiment analysis is the task of jointly predicting target entities and their associated sentiment information. Existing research efforts mostly regard this joint task as a sequence labeling problem, building models that can capture explicit structures in the output space. However, the importance of capturing implicit global structural information that resides in the input space is largely unexplored. In this work, we argue that both types of information (implicit and explicit structural information) are crucial for building a successful targeted sentiment analysis model. Our experimental results show that properly capturing both information is able to lead to better performance than competitive existing approaches. We also conduct extensive experiments to investigate our model's effectiveness and robustness.