Evidence-Based Text Classification System

An Evidence-Based Text Classification System is a text classification system that is an explainable NLP system implementing evidence-based classification algorithms to solve evidence-based text classification tasks.

• AKA: Evidence-Grounded Classification System, Interpretable Text Classifier.
• Context:
  • It can typically implement Evidence Extraction Modules with classification modules.
  • It can typically maintain Evidence-Decision Mappings through attention mechanisms.
  • It can typically enforce Faithfulness Constraints via architectural designs.
  • It can typically provide Classification Explanations through evidence visualizations.
  • It can typically support Interactive Evidence Review via user interfaces.
  • ...
  • It can often utilize Pre-trained Language Models for contextual understanding.
  • It can often employ Multi-Stage Architectures with evidence selection and classification stages.
  • It can often incorporate Evidence Ranking Algorithms for relevance scoring.
  • It can often integrate Confidence Calibration based on evidence strength.
  • ...
  • It can range from being a Pipeline Evidence-Based Classification System to being an End-to-End Evidence-Based Classification System, depending on its architectural integration.
  • It can range from being a Rule-Based Evidence Classification System to being a Neural Evidence Classification System, depending on its implementation approach.
  • ...
  • It can process Text Documents with evidence annotation capability.
  • It can output Classification Results with supporting evidence spans.
  • It can be evaluated by Evidence-Based Classification Performance Measures.
  • It can integrate with Document Management Systems for production deployment.
  • ...
• Example(s):
  • Legal Document Classification Systems, such as:
    • Contract Analysis System with clause highlighting features.
    • Regulatory Compliance System with requirement extraction.
  • Medical Text Classification Systems, such as:
    • Clinical Decision Support System with symptom identification.
    • Medical Literature Review System with evidence synthesis.
  • Fact Verification Systems, such as:
    • FEVER-Based Verification System with Wikipedia evidence retrieval.
    • Scientific Claim Verification System with paper citation extraction.
  • ...
• Counter-Example(s):
  • Black-Box Classification Systems, which lack evidence generation capability.
  • Pure Neural Classification Systems, which provide no interpretability mechanisms.
  • Template-Based Classification Systems, which use fixed patterns not evidence extraction.
• See: Explainable AI System, Text Classification System, Evidence Extraction System, Interpretable Machine Learning System.

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