Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction (2010.04640v2)

Abstract: 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.

• The paper presents a unified grid tagging scheme that transforms the extraction of opinion pairs and triplets into a single grid tagging task.
• It employs CNN, BiLSTM, and BERT models to leverage word-pair dependencies and overcome error propagation inherent in pipeline methods.
• The BERT-based implementation achieves state-of-the-art performance, demonstrating robust extraction of aspect-oriented opinion elements.

Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction

The paper "Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction" presents a novel approach for Aspect-oriented Fine-grained Opinion Extraction (AFOE). This task involves automatically extracting opinion pairs—comprising aspect terms and opinion terms—or opinion triplets—adding sentiment polarity to these pairs—from review text. Traditional methods typically decompose this complex task into multiple subtasks processed in a pipeline, which often leads to error propagation issues. To address these limitations, the authors propose a Grid Tagging Scheme (GTS) that enables end-to-end extraction using a unified grid tagging task.

Methodology

The cornerstone of this paper is the Grid Tagging Scheme, which transforms opinion pair and triplet extraction into a grid tagging problem. Each pair of words in a sentence is assigned a tag from a predefined set, capturing relationships pertinent to aspect terms, opinion terms, and sentiment polarity. The grid tagging task allows simultaneous extraction of all necessary opinion factors, circumventing the errors inherent in sequential pipeline approaches.

Key to the GTS approach is an innovative inference strategy that leverages dependencies among opinion factors for enhanced extraction accuracy. The scheme exploits potential indications between aspect and opinion terms, improving the robustness of opinion pair and triplet detection.

To evaluate GTS, the authors design three models based on different neural architectures—CNN, BiLSTM, and BERT—to accommodate varying contexts and textual representations. Experiments are conducted on established datasets for opinion pair and triplet extraction, allowing comprehensive performance validation against prominent baseline methods.

Results and Analysis

The empirical evaluation reveals that GTS, across its various model implementations, significantly outperforms traditional pipeline methods, achieving state-of-the-art performance for both opinion pair and triplet extraction tasks. The BERT-based GTS model, in particular, demonstrates superior capabilities due to BERT's contextual understanding of text, which aligns well with the tagging approach's reliance on contextual word-pair analysis.

The introduction of the inference strategy in GTS is shown to substantially enhance performance by capitalizing on interdependencies among opinion factors. This strategy ensures more robust detection and pairing of relevant terms, even in complex sentence structures where pipeline methods would typically falter.

Implications and Future Directions

The development of the Grid Tagging Scheme represents an advancement in the field of sentiment analysis, providing a more scalable and efficient framework for fine-grained opinion extraction. Its successful application to aspect-oriented opinion pair and triplet extraction suggests potential adaptability to other complex extraction tasks in natural language processing.

Future work could explore integrating GTS with domain-specific pre-trained models to further boost performance, particularly in niche domains with distinct linguistic characteristics. Moreover, expanding the grid tagging framework to accommodate multilingual datasets could broaden its applicability, making it a versatile tool for global sentiment analysis across diverse markets.

In conclusion, this paper contributes a significant methodological innovation to opinion extraction tasks by presenting a unified, end-to-end approach that addresses the critical issue of pipeline error propagation, ensuring more accurate and reliable sentiment analysis in practical applications.