VisNLP: A Visual-based Educational Support Platform for Learning Statistical NLP Analytics

Amorn Chokchaisiripakdee, Chun-Kit Ngan

2021

Abstract

We develop and implement a web-based, interactive visual NLP learning platform that enables novice learners to study the core processing components of statistical NLP analytics in sequence. More specifically, the contributions of this work are three-fold: (1) the ease of learners to access and use our platform through any web browser at no cost; (2) the interactive and dynamic visuals (e.g., mouseover events, collapsible tree diagrams, and animations) that enhance the study environment and learners’ engagement; and (3) the in-focus step-by-step process, using the job posting classification as an example, to demonstrate the core processing components of statistical NLP approaches.

Paper Citation

in Harvard Style

Chokchaisiripakdee A. and Ngan C. (2021). VisNLP: A Visual-based Educational Support Platform for Learning Statistical NLP Analytics. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP; ISBN 978-989-758-488-6, SciTePress, pages 224-232. DOI: 10.5220/0010318202240232

in Bibtex Style

@conference{ivapp21,
author={Amorn Chokchaisiripakdee and Chun-Kit Ngan},
title={VisNLP: A Visual-based Educational Support Platform for Learning Statistical NLP Analytics},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP},
year={2021},
pages={224-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010318202240232},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP
TI - VisNLP: A Visual-based Educational Support Platform for Learning Statistical NLP Analytics
SN - 978-989-758-488-6
AU - Chokchaisiripakdee A.
AU - Ngan C.
PY - 2021
SP - 224
EP - 232
DO - 10.5220/0010318202240232
PB - SciTePress