.png){kind=icon}
Return
Prompt-based data labeling method for aspect based sentiment analysis
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-024-02180-w.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
ABSA aims to extract aspect terms and corresponding sentiment from unstructured texts. Supervised approaches are widely used in existing ABSA models because of their model maturity, and most of them usually need large-scale training data to deal with over-fitting. However, in real scenarios, the labeled data is difficult to obtain, thus the performance is adversely influenced. To address these issues, this paper proposes a prompt-based data augmentation method, enabling it to overcome small data problems by expanding the sample size in the training corpus. Our approach computes the relationship between the prompt templates and unlabeled data and then assigns labels to expand the training data. To achieve this, we formulate it as a data filtering problem and implement it with Natural Language Inference models. The experimental results on four well-studied datasets demonstrate that our model not only achieves results on par with existing state-of-the-art data augmentation methods on a few occasions but also significantly improves the effectiveness of existing ABSA models on most occasions, indicating its strong robustness in various base ABSA models. Further discussion shows that prompt learning can help the model mark data from unlabeled datasets, which explains its effectiveness in data augmentation.
Keywords:
Natural language processing
Data augmentation
Prompt learning
Aspect based sentiment analysis
Neural network
Journal
IF:
2.7
Papers:
3.2K
Citations:
5.6K
Organization
Cited Papers
.png){kind=icon}
ShareSaveTreatment of variable valence in organic nomenclature (lambda convention) (Recommendations 1983)
OAAI
OAAIShareSaveShareSaveShareSaveShareSaveEvaluation of Diagnostic Criteria and Incidence of Acute Mountain Sickness in Preverbal Children☆OAAI
OAAIShareSaveShareSave
View more