An Algorithm for Estimating Answerers’ Performance and Improving Answer Quality Predictions in QA Forums

Yonas Woldemariam

2022

Abstract

In this study, a multi-components algorithm is developed for estimating answerer performance, largely from a syntactic representation of answer content. The resulting algorithm has been integrated into semantic based answer quality prediction models, and appears to significantly improve all testsets’ baseline results, in the best case scenario. Upto 86% accuracy and 84% F-measure are scored by these models. Also, answer quality classifiers yeild upto 100% recall and 98% precision. Following the transformation of joint syntactic-punctuation information into the identified expertise dimensions (e.g., authoritativeness, analytical, descriptiveness, completeness) that formally define answerer performance, extensive algorithm analyses have been carried on almost 142,246 answers extracted from diverse sets of 13 different QA forums. The analyses prove that incorporating competence information into answer quality models certainly leads to nearly perfect models. Moreover, we found out that the syntactic based algorithm with semantic based models yield better results than answer quality prediction modles built on shallow linguistic or meta-features presented in related works.

Paper Citation

in Harvard Style

Woldemariam Y. (2022). An Algorithm for Estimating Answerers’ Performance and Improving Answer Quality Predictions in QA Forums. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 106-113. DOI: 10.5220/0010783100003116

in Bibtex Style

@conference{icaart22,
author={Yonas Woldemariam},
title={An Algorithm for Estimating Answerers’ Performance and Improving Answer Quality Predictions in QA Forums},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={106-113},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010783100003116},
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 - An Algorithm for Estimating Answerers’ Performance and Improving Answer Quality Predictions in QA Forums
SN - 978-989-758-547-0
AU - Woldemariam Y.
PY - 2022
SP - 106
EP - 113
DO - 10.5220/0010783100003116