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.

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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