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FGSI: distant supervision for relation extraction method based on fine-grained semantic information.

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Summary
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This study introduces a novel approach to remote supervised relation extraction, focusing on key sentence semantics to reduce noise. The improved model enhances precision and recall for knowledge graph construction.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Data Science

Background:

  • Relation extraction is crucial for knowledge graph construction.
  • Remote supervision reduces manual annotation but introduces noisy data.
  • Noisy data significantly hinders the performance of relation extraction models.

Purpose of the Study:

  • To propose a novel relation extraction model that effectively handles noisy data in remote supervision.
  • To leverage key semantic information within sentences for improved relation extraction.
  • To enhance the accuracy and robustness of knowledge graph construction.

Main Methods:

  • Dividing sentences into three segments based on entity positions.
  • Employing intra-sentence attention mechanisms to identify fine-grained semantic features.
  • Improving the intra-bag attention mechanism with a threshold gate to filter noisy sentences.

Main Results:

  • The proposed model demonstrated improved performance in precision-recall curve, P@N value, and AUC value.
  • Effectively reduced the interference of irrelevant noise information in relation extraction.
  • Minimized the impact of noisy data on the relation extraction model.

Conclusions:

  • The proposed method effectively utilizes key semantic information to mitigate noise in remote supervised relation extraction.
  • The model shows significant improvements over existing methods, validating its effectiveness.
  • This approach contributes to more accurate and efficient knowledge graph construction.