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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Graph Adaptation Network with Domain-Specific Word Alignment for Cross-Domain Relation Extraction.

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  • 1School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China.

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Summary

This study introduces a novel tripartite graph architecture for cross-domain relation extraction, effectively transferring non-local features when target domains lack labeled data. The proposed method significantly improves performance over existing approaches.

Keywords:
domain adaptationgraph convolution networknon-local featuresrelation extraction

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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.