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Relation extraction: advancements through deep learning and entity-related features.

Youwen Zhao1, Xiangbo Yuan1, Ye Yuan1

  • 1Digital and information management center, Kweichow Moutai Co., Ltd, Zunyi, 564501 Guizhou China.

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Summary
This summary is machine-generated.

This study introduces a novel approach for relation extraction, fusing convolutional neural networks and graph convolution neural networks to capture entity semantics and structure. The method significantly improves relation extraction performance on multiple datasets.

Keywords:
Fusion featureGraph convolutionRelation extractionSemantic structure

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Relation extraction is vital for understanding text but faces challenges due to limited semantic and structural information around entity pairs.
  • Existing methods struggle to effectively leverage both semantic and structural features for accurate relation identification.

Purpose of the Study:

  • To develop a robust approach for relation extraction by effectively fusing entity-related semantic and structural features.
  • To enhance the extraction of high-order abstract features for improved relation identification accuracy.

Main Methods:

  • A novel approach combining Convolutional Neural Networks (CNNs) and Graph Convolutional Neural Networks (GCNs) is proposed.
  • Entity-related features are fused to generate comprehensive 'fusion features'.
  • A deep learning framework is applied to extract high-order abstract features for relation extraction.

Main Results:

  • The proposed approach achieved high F1-scores on three public datasets: 77.70% on ACE05 English, 90.12% on ACE05 Chinese, and 68.84% on SanWen.
  • Experimental results demonstrate the effectiveness and robustness of the fusion-based deep learning method.
  • The approach successfully captures crucial semantics and structure for improved relation extraction.

Conclusions:

  • The proposed method effectively addresses the limitations of extracting relations by fusing semantic and structural information.
  • The fusion of CNNs and GCNs provides a powerful framework for deep feature extraction in relation extraction tasks.
  • The approach shows significant promise for advancing the field of relation extraction in various natural language processing applications.