  Aspect term extraction is one of the important subtasks in aspect-based
sentiment analysis. Previous studies have shown that using dependency tree
structure representation is promising for this task. However, most dependency
tree structures involve only one directional propagation on the dependency
tree. In this paper, we first propose a novel bidirectional dependency tree
network to extract dependency structure features from the given sentences. The
key idea is to explicitly incorporate both representations gained separately
from the bottom-up and top-down propagation on the given dependency syntactic
tree. An end-to-end framework is then developed to integrate the embedded
representations and BiLSTM plus CRF to learn both tree-structured and
sequential features to solve the aspect term extraction problem. Experimental
results demonstrate that the proposed model outperforms state-of-the-art
baseline models on four benchmark SemEval datasets.
