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Summary

This study introduces Mathematical Entity Relation Extraction (MERE) to understand math text, achieving 99.39% accuracy with BERT. Explainable AI (XAI) methods like SHAP provide insights into model predictions for better trust.

Keywords:
Entity entity relationMathematical enttiesSHAPTransformer-based learningXAI

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Mathematical text presents unique challenges for natural language understanding due to specialized entities and complex relationships.
  • Existing methods struggle to accurately interpret the structure and meaning within mathematical problems.

Purpose of the Study:

  • To formulate mathematical problem interpretation as a Mathematical Entity Relation Extraction (MERE) task.
  • To develop and evaluate transformer-based models for automated MERE.
  • To enhance model interpretability using Explainable Artificial Intelligence (XAI).

Main Methods:

  • Formulation of MERE task: operands as entities, operators as relationships.
  • Application of transformer-based models, specifically Bidirectional Encoder Representations from Transformers (BERT).
  • Integration of Explainable Artificial Intelligence (XAI) techniques, notably Shapley Additive Explanations (SHAP).

Main Results:

  • BERT achieved state-of-the-art performance with 99.39% accuracy in MERE.
  • SHAP analysis provided insights into feature importance, explaining model predictions.
  • Demonstrated the effectiveness of combining transformer models with XAI for mathematical text understanding.

Conclusions:

  • The proposed MERE framework offers an effective and interpretable solution for mathematical text understanding.
  • The approach supports advancements in automated problem solving, knowledge graph construction, and intelligent educational systems.
  • Explainability enhances trust and transparency in AI models applied to complex domains like mathematics.