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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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A storytree-based model for inter-document causal relation extraction from news articles.

Chong Zhang1, Jiagao Lyu1, Ke Xu1

  • 1State Key Lab of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing, China.

Knowledge and Information Systems
|November 9, 2022
PubMed
Summary
This summary is machine-generated.

Discovering causal relations between news articles is crucial for understanding news development. A new inter-document model using storytree information significantly improves causal relation extraction accuracy over existing methods.

Keywords:
Causal relationConstraintNews articleRelation classification

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Area of Science:

  • Natural Language Processing
  • Information Extraction
  • Computational Linguistics

Background:

  • News articles are increasingly prevalent online, necessitating methods to understand their interconnections.
  • Current relation extraction models often struggle with the complexity and length of inter-document analysis.
  • Existing inter-document approaches relying solely on similarity metrics have performance limitations.

Discussion:

  • This paper introduces an inter-document model leveraging storytree information for causal relation extraction between news articles.
  • The model integrates storytree information into an integer linear programming (ILP) framework with custom constraints.
  • The proposed approach addresses limitations of intra-document models and similarity-based inter-document methods.

Key Insights:

  • The storytree-based ILP model effectively extracts causal relations between news articles.
  • Experimental results demonstrate superior performance compared to traditional machine learning and deep learning models, with over 5% F1 improvement.
  • Specific storytree constraints contribute significantly to improved extraction accuracy, with varying impacts across datasets.

Outlook:

  • Further research can explore optimizing the influence of different link features within the storytree framework.
  • The model's adaptability to diverse news datasets suggests potential for broader applications in news analysis.
  • Investigating the integration of additional contextual information could further enhance inter-document causal relation extraction.