Keskeiset käsitteet
The Extensible Multi-Granularity Fusion (EMGF) network integrates diverse linguistic and structural features efficiently, resulting in superior performance in Aspect-Based Sentiment Analysis (ABSA).
Tiivistelmä
The EMGF model addresses the challenge of integrating multiple granularity features in ABSA. It combines dependency and constituent syntactic information, semantic attention, and external knowledge graphs to achieve a cumulative effect without additional computational expenses. Experimental results confirm the superiority of EMGF over existing methods on SemEval 2014 and Twitter datasets.
Key Points:
- ABSA evaluates sentiment expressions within text.
- Previous studies integrated external knowledge to enhance semantic features.
- Recent research focused on Graph Neural Networks for syntactic analysis.
- Dependency trees establish connections among words, while constituent trees provide phrase segmentation.
- Single-granularity features are insufficient for capturing rich information.
- EMGF integrates various granularities efficiently with multi-anchor triplet learning and orthogonal projection.
- The model surpasses state-of-the-art methods in ABSA tasks.
Tilastot
Experimental findings on SemEval 2014 and Twitter datasets confirm EMGF’s superiority over existing ABSA methods.
Lainaukset
"Most current methods use complex and inefficient techniques to integrate diverse types of knowledge."
"In this paper, we introduce a novel architecture called the Extensible Multi-Granularity Fusion Network model (EMGF) to address the aforementioned challenges."