Classifying MathML Expressions by Multilayer Perceptron

Yuma NAGAO; Nobutaka SUZUKI

doi:10.1587/transinf.2017EDL8211

Classifying MathML Expressions by Multilayer Perceptron

Yuma NAGAO, Nobutaka SUZUKI

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MathML is a standard markup language for describing math expressions. MathML consists of two sets of elements: Presentation Markup and Content Markup. The former is widely used to display math expressions in Web pages, while the latter is more suited to the calculation of math expressions. In this letter, we focus on the former and consider classifying Presentation MathML expressions. Identifying the classes of given Presentation MathML expressions is helpful for several applications, e.g., Presentation to Content MathML conversion, text-to-speech, and so on. We propose a method for classifying Presentation MathML expressions by using multilayer perceptron. Experimental results show that our method classifies MathML expressions with high accuracy.

Publication: IEICE TRANSACTIONS on Information Vol.E101-D No.7 pp.1954-1958

Publication Date: 2018/07/01

Publicized: 2018/04/04

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2017EDL8211

Type of Manuscript: LETTER

Category: Data Engineering, Web Information Systems

Authors

Yuma NAGAO
University of Tsukuba
Nobutaka SUZUKI
University of Tsukuba

Keyword

MathML, classification, multilayer perceptron

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Yuma NAGAO, Nobutaka SUZUKI, "Classifying MathML Expressions by Multilayer Perceptron" in IEICE TRANSACTIONS on Information, vol. E101-D, no. 7, pp. 1954-1958, July 2018, doi: 10.1587/transinf.2017EDL8211.
Abstract: MathML is a standard markup language for describing math expressions. MathML consists of two sets of elements: Presentation Markup and Content Markup. The former is widely used to display math expressions in Web pages, while the latter is more suited to the calculation of math expressions. In this letter, we focus on the former and consider classifying Presentation MathML expressions. Identifying the classes of given Presentation MathML expressions is helpful for several applications, e.g., Presentation to Content MathML conversion, text-to-speech, and so on. We propose a method for classifying Presentation MathML expressions by using multilayer perceptron. Experimental results show that our method classifies MathML expressions with high accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDL8211/_p

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@ARTICLE{e101-d_7_1954,
author={Yuma NAGAO, Nobutaka SUZUKI, },
journal={IEICE TRANSACTIONS on Information},
title={Classifying MathML Expressions by Multilayer Perceptron},
year={2018},
volume={E101-D},
number={7},
pages={1954-1958},
abstract={MathML is a standard markup language for describing math expressions. MathML consists of two sets of elements: Presentation Markup and Content Markup. The former is widely used to display math expressions in Web pages, while the latter is more suited to the calculation of math expressions. In this letter, we focus on the former and consider classifying Presentation MathML expressions. Identifying the classes of given Presentation MathML expressions is helpful for several applications, e.g., Presentation to Content MathML conversion, text-to-speech, and so on. We propose a method for classifying Presentation MathML expressions by using multilayer perceptron. Experimental results show that our method classifies MathML expressions with high accuracy.},
keywords={},
doi={10.1587/transinf.2017EDL8211},
ISSN={1745-1361},
month={July},}

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TY - JOUR
TI - Classifying MathML Expressions by Multilayer Perceptron
T2 - IEICE TRANSACTIONS on Information
SP - 1954
EP - 1958
AU - Yuma NAGAO
AU - Nobutaka SUZUKI
PY - 2018
DO - 10.1587/transinf.2017EDL8211
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2018
AB - MathML is a standard markup language for describing math expressions. MathML consists of two sets of elements: Presentation Markup and Content Markup. The former is widely used to display math expressions in Web pages, while the latter is more suited to the calculation of math expressions. In this letter, we focus on the former and consider classifying Presentation MathML expressions. Identifying the classes of given Presentation MathML expressions is helpful for several applications, e.g., Presentation to Content MathML conversion, text-to-speech, and so on. We propose a method for classifying Presentation MathML expressions by using multilayer perceptron. Experimental results show that our method classifies MathML expressions with high accuracy.
ER -