Technical Report -- A Context-Sensitive Multi-Level Similarity Framework for First-Order Logic Arguments: An Axiomatic Study

1. 1Build a comprehensive framework for FOL argument similarity using an extended axiomatic foundation
2. 2Apply the framework to account for structured content in First-Order Logic
3. 3Evaluate the framework's performance in argument aggregation and enthymeme decoding
4. 4Compare the results with existing approaches in propositional logic
5. 5Integrate 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