Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
The paper "Graph Convolution over Pruned Dependency Trees Improves Relation Extraction" by Yuhao Zhang, Peng Qi, and Christopher D. Manning examines the application of graph convolutional networks (GCNs) to the task of relation extraction (RE). The authors propose a novel model that leverages syntactic dependency trees to efficiently capture and process long-range word dependencies, ultimately yielding state-of-the-art results on benchmark datasets.
Overview
Relation extraction involves determining whether a specific relation exists between two identified entities within a sentence. Dependency trees provide a valuable mechanism for encapsulating syntactic dependencies that traditional sequence-based models may overlook. However, previous dependency-based models struggled either by pruning too aggressively, thus losing crucial contextual information, or by incurring excessive computational costs due to difficulty in parallelizing across tree structures.
The primary contributions of this paper are twofold:
- GCNs Tailored for Relation Extraction: The authors extend GCNs to handle arbitrary dependency structures in a manner that supports efficient parallel processing.
- Path-Centric Pruning Strategy: A novel pruning technique that retains relevant contextual words close to the shortest path between entities, thus balancing information retention with noise reduction.
Model Architecture
Graph Convolutional Networks over Dependency Trees
The core innovation lies in adapting GCNs to operate over dependency trees. Each tree is converted into an adjacency matrix, which the GCN uses to aggregate and propagate information through graph nodes representing tokens. The GCN layers are designed to accommodate self-loops and normalized activations to mitigate issues related to node degree variability, ensuring consistent node representation magnitudes.
Encoding Relations with GCN
The network employs multiple GCN layers to derive hidden representations for each token. These representations are combined through a max-pooling operation to form a comprehensive sentence-level embedding. Additionally, the model extracts entity-specific embeddings by pooling information over tokens representing the subject and object entities. The final embedding, used for relation classification, is a concatenation of the sentence-level and entity-level representations fed into a feedforward neural network.
Contextualized GCN
To address potential parsing errors and incorporate word-order information, the authors introduce a variant termed Contextualized GCN (C-GCN). This variant preprocesses input token vectors via a bi-directional LSTM, enhancing the contextual robustness of the GCN layers.
Path-Centric Pruning
The pruning strategy is a critical enhancement, retaining tokens within a distance K from the shortest dependency path between entities. Empirically, K=1 provides an optimal balance, ensuring relevant off-path information is preserved while extraneous content is discarded.
Experimental Results
The models were evaluated on two datasets: TACRED and SemEval 2010 Task 8. Significant improvements were observed:
- TACRED: The C-GCN achieved state-of-the-art performance with an F1 score of 66.4, surpassing previous dependency-based and sequence-based models. Combining C-GCN with PA-LSTM through simple prediction interpolation further improved the F1 score to 68.2.
- SemEval: The C-GCN outperformed existing models, demonstrating robustness under both with-mention and mask-mention evaluation approaches.
Analysis and Implications
Through detailed ablation studies, the paper highlights the significance of each model component. The entity representations and feedforward layers contribute substantially to performance, reinforcing the importance of properly encoding syntactic dependencies.
A comparative analysis revealed that GCN-based models are particularly effective at handling long-range dependencies in sentences, while sequence models like PA-LSTM excel at leveraging local word patterns, even under suboptimal parsing conditions. This complementary strength underscores the potential of hybrid modeling approaches.
Future Directions
The findings suggest several avenues for future research:
- Exploring More Complex Pruning Strategies: Further refining pruning techniques could offer even better performance by dynamically adjusting the pruning distance based on syntactic importance.
- Integration with Other Neural Models: Further integration of GCNs with advanced contextual LLMs could provide richer syntactic and semantic representations, potentially boosting performance on more diverse datasets.
- Application Beyond Relation Extraction: The proposed GCN architecture could benefit other NLP tasks that rely heavily on syntactic structure, such as semantic role labeling or event extraction.
Overall, this paper makes significant strides in leveraging graph convolutional networks for relation extraction, presenting a robust, efficient approach that delivers state-of-the-art performance through innovative use of syntactic dependencies and thoughtful pruning strategies.