This paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example.
Aspect-based sentiment analysis (ABSA) typically requires in-domain annotated data for supervised training/fine-tuning.
Experiments based on simulated and real-world data show that the proposed split-and-conquer approach has comparable statistical performance with the global estimator based on the full dataset, if the latter is feasible.
Classifying and resolving coreferences of objects (e. g., product names) and attributes (e. g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance.
Although existing clustering-based methods are known for their satisfactory performance and computational efficiency, their convergence properties and optimal targets remain unknown.