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Updated: May 26, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Open-world semi-supervised relation extraction.

Diange Zhou1, Yilin Duan1, Shengwen Li2

  • 1School of Computer Science, China University of Geosciences, Wuhan, 430078, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 23, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new open-world semi-supervised relation extraction method (SIND) that effectively identifies both known and novel relationships from text, improving accuracy in real-world applications.

Keywords:
Contrastive learningMaximum entropy theoryRelation extractionSemi-supervised

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Semi-supervised relation extraction methods improve accuracy by using labeled and unlabeled data.
  • Existing methods fail in real-world scenarios due to the closed-world assumption, which excludes novel relationships.

Purpose of the Study:

  • Propose an open-world semi-supervised relation extraction task to handle novel relationships.
  • Introduce a novel method, SIND, for simultaneous extraction of seen and novel relations.

Main Methods:

  • SIND employs contrastive learning for enhanced semantic relation representation.
  • A cluster-aware method discovers novel relations using feature space similarity.
  • Maximum entropy theory addresses learning imbalances from unlabeled novel classes.

Main Results:

  • SIND significantly outperforms baseline models on three benchmark datasets.
  • Demonstrates effectiveness in extracting both existing and previously unseen relationships.

Conclusions:

  • SIND offers a robust solution for discovering relationships in unannotated data within open-world settings.
  • Presents a valuable reference for NLP tasks like text classification and named entity recognition.