Metot
Key Features
Tech Stack
Key Features
Tech Stack
Proper system requires: Deep NLP: Discourse parsing, semantic role labeling, entailment; Structured reasoning: AAF, probabilistic argumentation, Bayesian aggregation; Domain knowledge: Evidence hierarchies, causal inference, statistical meta-analysis; Explainability: Attention visualization, counterfactual reasoning, gradient-based saliency
1. Toulmin Model Analysis Each argument is analyzed for its full structure, not just PRO/CON:
• Claim: The specific assertion
• Evidence: Supporting facts, data, sources
• Warrant: The reasoning connecting evidence to claim
• Strength Score: 1-10 based on evidence quality, warrant clarity, and fallacy presence
2. Dialectical Mapping with Recursive Response Structure Contrary to “structural blindness,” our system tracks how arguments respond to each other recursively:
Argument 1 (Supporting) └── Response 1.1 (Opposing - objection) └── Response 1.1.1 (Supporting - rebuttal) └── Response 1.1.1.1 (Opposing - counter-rebuttal)
This captures unlimited depth of dialectical exchanges.
3. Logical Fallacy Detection Contrary to “fallacy tolerance,” we detect: circular reasoning, ad hominem, straw man, false dichotomy, hasty generalization, and others.
4. Context-Aware Type Assignment Argument type (supporting/opposing) is determined relative to the author’s thesis, not absolute. If the author criticizes Theory X, arguments against X are classified as “supporting.” This addresses semantic context.
5. Self-Validation Layer Before output, the system validates:
• Argument count (academic texts typically have 5-15+ distinct arguments)
• Depth check (most academic texts have 2-4 levels) • Balance check (detects one-sidedness)
• Type accuracy verification What We Acknowledge: • Single-pass analysis (no iterative refinement yet) • General academic analysis rather than domain-specific ontologies
I appreciate critical feedback, but the system is not vote counting. Feel free to test with a demo account.
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