Graph Adaptation Network with Domain-Specific Word Alignment for Cross-Domain Relation Extraction

Area of Science:

• Natural Language Processing
• Machine Learning
• Artificial Intelligence

Background:

• Cross-domain relation extraction is crucial for domains with limited labeled data.
• Existing methods struggle to transfer non-local and non-sequential features like word co-occurrence.
• This limitation hinders effective relation extraction in data-scarce target domains.

Purpose of the Study:

• To propose a novel tripartite graph architecture for cross-domain relation extraction.
• To effectively adapt non-local features in target domains lacking labeled data.
• To improve the transfer of domain-specific features for enhanced relation extraction.

Main Methods:

• Developed a tripartite graph architecture using domain words as nodes.
• Modeled co-occurrence relations between domain-specific and domain-independent words.
• Employed graph convolutions with innovative edge weights (fixed and dynamic) to capture global non-local features.

Main Results:

• Successfully adapted non-local features for cross-domain relation extraction.
• Fine-tuned word representations by propagating domain-specific information.
• Achieved significant performance improvements over state-of-the-art models on ACE2005 datasets.

Conclusions:

• The proposed tripartite graph architecture effectively addresses limitations in cross-domain relation extraction.
• The method demonstrates superior performance by transferring critical non-local and non-sequential features.
• This approach offers a promising solution for relation extraction in low-resource domains.