  Lack of labeled training data is a major bottleneck for neural network based
aspect and opinion term extraction on product reviews. To alleviate this
problem, we first propose an algorithm to automatically mine extraction rules
from existing training examples based on dependency parsing results. The mined
rules are then applied to label a large amount of auxiliary data. Finally, we
study training procedures to train a neural model which can learn from both the
data automatically labeled by the rules and a small amount of data accurately
annotated by human. Experimental results show that although the mined rules
themselves do not perform well due to their limited flexibility, the
combination of human annotated data and rule labeled auxiliary data can improve
the neural model and allow it to achieve performance better than or comparable
with the current state-of-the-art.
