Human Behavior Assessment using Ensemble Models

Abdullah Faiz Ur Rahman Khilji, Rituparna Khaund, Utkarsh Sinha


Abstract
Behavioral analysis is a pertinent step in today’s automated age. It is important to judge a statement on a variety of parameters before reaching a valid conclusion. In today’s world of technology and automation, Natural language processing tools have benefited from growing access to data in order to analyze the context and scenario. A better understanding of human behaviors would empower a range of automated tools to provide users a customized experience. For precise analysis, behavior understanding is important. We have experimented with various machine learning techniques, and have obtained a maximum private score of 0.1033 with a public score of 0.1733. The methods are described as part of the ALTA 2020 shared task. In this work, we have enlisted our results and the challenges faced to solve the problem of the human behavior assessment.
Anthology ID:
2020.alta-1.20
Volume:
Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association
Month:
December
Year:
2020
Address:
Virtual Workshop
Editors:
Maria Kim, Daniel Beck, Meladel Mistica
Venue:
ALTA
SIG:
Publisher:
Australasian Language Technology Association
Note:
Pages:
140–144
Language:
URL:
https://aclanthology.org/2020.alta-1.20
DOI:
Bibkey:
Cite (ACL):
Abdullah Faiz Ur Rahman Khilji, Rituparna Khaund, and Utkarsh Sinha. 2020. Human Behavior Assessment using Ensemble Models. In Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association, pages 140–144, Virtual Workshop. Australasian Language Technology Association.
Cite (Informal):
Human Behavior Assessment using Ensemble Models (Khilji et al., ALTA 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.alta-1.20.pdf