Abstract
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.