Usage of Stacked Long Short-Term Memory for Recognition of 3D Analytic Geometry Elements

Anca-Elena Iordan

2022

Abstract

For accomplish automatic solving, the capacity to comprehend problems of 3D analytic geometry formulated in natural language is a laborious and stimulating open research theme. For this reason, this research work attempts the achievement of a parser compounded of two important parts: the parsing module and the learning module. The accomplishment of the parsing module requires the design of a method for engendering the series of actions required to acquire the UCCA graph corresponding with a phrase from a 3D analytic geometry problem. In order to design the learning module, is used a recurrent neural network of the Stacked Long Short-Term Memory category, thereby being realized an automatic parsing system. To achieve this goal, the proposed novel solution is accomplished through the usage of Python programming language.

Paper Citation

in Harvard Style

Iordan A. (2022). Usage of Stacked Long Short-Term Memory for Recognition of 3D Analytic Geometry Elements. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 745-752. DOI: 10.5220/0010898900003116

in Bibtex Style

@conference{icaart22,
author={Anca-Elena Iordan},
title={Usage of Stacked Long Short-Term Memory for Recognition of 3D Analytic Geometry Elements},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={745-752},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010898900003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Usage of Stacked Long Short-Term Memory for Recognition of 3D Analytic Geometry Elements
SN - 978-989-758-547-0
AU - Iordan A.
PY - 2022
SP - 745
EP - 752
DO - 10.5220/0010898900003116