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Updated: Sep 22, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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Error correction of semantic mathematical expressions based on bayesian algorithm.

Xue Wang1,2, Fang Yang1,2, Hongyuan Liu1,2

  • 1School of Cyber Security and Computer, Hebei University, Baoding 071002, China.

Mathematical Biosciences and Engineering : MBE
|May 23, 2022
PubMed
Summary

A new Bayesian error correction algorithm significantly improves the semantic accuracy of mathematical expressions. This method enhances information retrieval by correcting errors in converting presentation MathML to content MathML.

Keywords:
Bayesian algorithmcontent MathMLerror correctionmathematical expressionspresentation MathML

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Area of Science:

  • Computer Science
  • Computational Linguistics
  • Digital Libraries

Background:

  • Mathematical expressions in electronic documents often lack semantic information in presentation MathML.
  • Converting presentation MathML to content MathML using rule mapping is a common but error-prone method.
  • Semantic information is crucial for effective information retrieval and similarity calculations.

Purpose of the Study:

  • To develop and evaluate a Bayesian error correction algorithm for semantic conversion of mathematical expressions.
  • To address the semantic inaccuracies arising from direct rule-based conversions between presentation and content MathML.

Main Methods:

  • A Bayesian error correction algorithm was developed to refine semantic information.
  • The algorithm's parameters were optimized using NTCIR dataset expressions (presentation and content MathML).
  • Performance was evaluated on expressions from CNKI website documents.

Main Results:

  • The rule mapping method achieved an average F1 score of 0.239.
  • The proposed Bayesian error correction method achieved a significantly higher average F1 score of 0.881.
  • An average error correction rate of 0.853 was observed with the Bayesian approach.

Conclusions:

  • The Bayesian error correction algorithm substantially improves the accuracy of semantic information extraction from mathematical expressions.
  • This method offers a robust solution for enhancing the semantic understanding of mathematical content in digital libraries and information retrieval systems.