Technical Report -- A Context-Sensitive Multi-Level Similarity Framework for First-Order Logic Arguments: An Axiomatic Study
Learn a context-sensitive multi-level similarity framework for First-Order Logic arguments and its axiomatic study to improve argument aggregation and enthymeme decoding
- Build a comprehensive framework for FOL argument similarity using an extended axiomatic foundation
- Apply the framework to account for structured content in First-Order Logic
- Evaluate the framework's performance in argument aggregation and enthymeme decoding
- Compare the results with existing approaches in propositional logic
- Integrate the framework with machine learning models to improve natural language processing tasks
Researchers and developers in AI and logic can benefit from this framework to enhance their argumentation systems, while data scientists and software engineers can apply this knowledge to improve their natural language processing and machine learning models
💡 A context-sensitive multi-level similarity framework can effectively account for structured content in First-Order Logic arguments, enhancing argumentation systems and natural language processing tasks
🤖 Introducing a context-sensitive multi-level similarity framework for First-Order Logic arguments! 📚 Improve argument aggregation and enthymeme decoding with this axiomatic study
Key Takeaways
Learn a context-sensitive multi-level similarity framework for First-Order Logic arguments and its axiomatic study to improve argument aggregation and enthymeme decoding
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Abstract:
arXiv:2604.12534v1 Announce Type: new Abstract: Similarity in formal argumentation has recently gained attention due to its significance in problems such as argument aggregation in semantics and enthymeme decoding. While existing approaches focus on propositional logic, we address the richer setting of First-Order Logic (FOL), where similarity must account for structured content. We introduce a comprehensive framework for FOL argument similarity, built upon: (1) an extended axiomatic foundation;
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