Graph Diffusion Transformer for Aspect Sentiment Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing aspect-level sentiment classification methods struggle to accurately capture interactions between distant aspect terms and their context, leading to suboptimal performance due to the over-smoothing issue in deeper graph attention network (GAT) models.
Innovation Solution
The proposed system employs a graph diffusion transformer (GDT) model that converts sentences into dependency trees, calculates multi-hop attention using a diffusion attention mechanism, and updates node embeddings to effectively classify aspect terms by aggregating information from multiple hops in a single layer, alleviating the over-smoothing problem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deeper GAT layers are used to capture long-range syntactic relations, then the model can access distant opinion context, but the over-smoothing problem occurs and performance deteriorates
Solution Approach 1:
The patent segments the attention mechanism into two distinct components: self-attention for local context capture and graph attention for syntactic relation propagation. This segmentation allows each mechanism to operate independently at optimal depths, preventing the over-smoothing that occurs when using deep GAT layers alone.
Solution Approach 2:
The patent merges self-attention and graph attention mechanisms into a unified transformer architecture. By combining these two attention types, the model achieves both local contextual understanding and long-range syntactic relation capture without the over-smoothing problem of deep GAT, as the self-attention component provides a stabilizing effect.
2Measurement precision
If more GAT layers are stacked to pass information between distant nodes, then long-range relations can be captured, but computational complexity increases and performance gains diminish
Solution Approach 1:
The patent introduces a new dimension to the attention mechanism by combining self-attention (handling local context) with graph attention (handling syntactic relations). This dimensional expansion allows the model to capture long-range relations through the graph attention component without needing to stack many GAT layers, as the self-attention component provides an additional channel for information flow.
3Measurement precision
If traditional GAT models are used for aspect-level sentiment classification, then syntactic information can be captured, but the model fails to accurately classify aspects far from opinion context
Solution Approach 1:
The patent introduces self-attention as an intermediary mechanism that operates alongside graph attention. The self-attention component acts as a mediator that captures local contextual information and prevents information loss, while the graph attention component propagates syntactic relations. This intermediary self-attention mechanism ensures that aspects far from opinion context can still be accurately classified by recovering lost contextual information.
Data Source
AI summary
System and method for aspect-level sentiment classification. The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: receive a sentence having a labeled aspect term and context; convert the sentence into a dependency tree graph; calculate an attention matrix of the dependency tree graph based on one-hop attention between any two nodes of the graph; calculate multi-head attention diffusion for any two nodes from the attention matrix; obtain updated embedding of the graph using the multi-head diffusion attention; classify the aspect term based on the updated embedding of the graph to obtain predicted classification of the aspect term; calculate loss function based on the predicted classification and the ground truth label of the aspect term; and adjust parameters of models in the computer executable code based on the loss function.