Opinion Tree Parsing for Aspect-based Sentiment Analysis
Xiaoyi Bao Xiaotong Jiang Zhongqing Wang Yue Zhang Guodong Zhou
Extracting sentiment elements using pre-trained generative models has
recently led to large improvements in aspect-based sentiment analysis
benchmarks. However, these models always need large-scale computing resources,
and they also ignore explicit modeling of structure between sentiment elements.
To address these challenges, we propose an opinion tree parsing model, aiming
to parse all the sentiment elements from an opinion tree, which is much faster,
and can explicitly reveal a more comprehensive and complete aspect-level
sentiment structure. In particular, we first introduce a novel context-free
opinion grammar to normalize the opinion tree structure. We then employ a
neural chart-based opinion tree parser to fully explore the correlations among
sentiment elements and parse them into an opinion tree structure. Extensive
experiments show the superiority of our proposed model and the capacity of the
opinion tree parser with the proposed context-free opinion grammar. More
importantly, the results also prove that our model is much faster than previous
models.