Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction
This addresses error propagation and inconvenience in real-world scenarios for extracting aspect and opinion terms from reviews, though it is incremental as it builds on existing tagging schemes.
The authors tackled the Aspect-oriented Fine-grained Opinion Extraction (AFOE) task by proposing a Grid Tagging Scheme (GTS) to unify it into an end-to-end process, achieving state-of-the-art performance with significant improvements over strong baselines in experiments on opinion pair and triplet extraction datasets.
Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims at extracting aspect terms and opinion terms from review in the form of opinion pairs or additionally extracting sentiment polarity of aspect term to form opinion triplet. Because of containing several opinion factors, the complete AFOE task is usually divided into multiple subtasks and achieved in the pipeline. However, pipeline approaches easily suffer from error propagation and inconvenience in real-world scenarios. To this end, we propose a novel tagging scheme, Grid Tagging Scheme (GTS), to address the AFOE task in an end-to-end fashion only with one unified grid tagging task. Additionally, we design an effective inference strategy on GTS to exploit mutual indication between different opinion factors for more accurate extractions. To validate the feasibility and compatibility of GTS, we implement three different GTS models respectively based on CNN, BiLSTM, and BERT, and conduct experiments on the aspect-oriented opinion pair extraction and opinion triplet extraction datasets. Extensive experimental results indicate that GTS models outperform strong baselines significantly and achieve state-of-the-art performance.