Classifying Mathematical Expressions Written in MathML
Summary :
In this paper, we study how to automatically classify mathematical expressions written in MathML (Mathematical Markup Language). It is an essential preprocess to resolve analysis problems originated from multi-meaning mathematical symbols. We first define twelve equation classes based on chapter information of mathematics textbooks and then conduct various experiments. Experimental results show an accuracy of 94.75%, by employing the feature combination of tags, operators, strings, and “identifier & operator” bigram.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.10 pp.2560-2563
- Publication Date
- 2012/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.2560
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Shinil KIM, Seon YANG, Youngjoong KO, "Classifying Mathematical Expressions Written in MathML" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 10, pp. 2560-2563, October 2012, doi: 10.1587/transinf.E95.D.2560.
Abstract: In this paper, we study how to automatically classify mathematical expressions written in MathML (Mathematical Markup Language). It is an essential preprocess to resolve analysis problems originated from multi-meaning mathematical symbols. We first define twelve equation classes based on chapter information of mathematics textbooks and then conduct various experiments. Experimental results show an accuracy of 94.75%, by employing the feature combination of tags, operators, strings, and “identifier & operator” bigram.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.2560/_p
Copy
@ARTICLE{e95-d_10_2560,
author={Shinil KIM, Seon YANG, Youngjoong KO, },
journal={IEICE TRANSACTIONS on Information},
title={Classifying Mathematical Expressions Written in MathML},
year={2012},
volume={E95-D},
number={10},
pages={2560-2563},
abstract={In this paper, we study how to automatically classify mathematical expressions written in MathML (Mathematical Markup Language). It is an essential preprocess to resolve analysis problems originated from multi-meaning mathematical symbols. We first define twelve equation classes based on chapter information of mathematics textbooks and then conduct various experiments. Experimental results show an accuracy of 94.75%, by employing the feature combination of tags, operators, strings, and “identifier & operator” bigram.},
keywords={},
doi={10.1587/transinf.E95.D.2560},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Classifying Mathematical Expressions Written in MathML
T2 - IEICE TRANSACTIONS on Information
SP - 2560
EP - 2563
AU - Shinil KIM
AU - Seon YANG
AU - Youngjoong KO
PY - 2012
DO - 10.1587/transinf.E95.D.2560
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2012
AB - In this paper, we study how to automatically classify mathematical expressions written in MathML (Mathematical Markup Language). It is an essential preprocess to resolve analysis problems originated from multi-meaning mathematical symbols. We first define twelve equation classes based on chapter information of mathematics textbooks and then conduct various experiments. Experimental results show an accuracy of 94.75%, by employing the feature combination of tags, operators, strings, and “identifier & operator” bigram.
ER -
English
