  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.
