Uncertain Formal Concept Analysis for the Study of a Text Corpus

Guillaume Petiot

2024

Abstract

The analysis of a corpus by an expert takes a relatively long time. The development of digital tools made it possible to generate instantly a summary of information contained in the corpus. In this paper, we will focus on the contribution of formal concept analysis (FCA) to the analysis of a corpus. FCA makes it possible to build a model also called the Hasse diagram which can be queried to find relevant formal concepts. Uncertainties can be present in all steps of the processing from the corpus processing to the visualization of the results. Indeed, if the words of the corpus are misspelled or additional quantitative variables are associated with the corpus, then uncertainties can appear. Uncertainties may also arise in queries when human knowledge is imprecise. Possibility theory allows us to represent and process these imperfections. The combination of textual analysis solutions and FCA allow us to present more relevant results that take into consideration uncertainties.

Paper Citation

in Harvard Style

Petiot G. (2024). Uncertain Formal Concept Analysis for the Study of a Text Corpus. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 218-225. DOI: 10.5220/0012316400003636

in Bibtex Style

@conference{icaart24,
author={Guillaume Petiot},
title={Uncertain Formal Concept Analysis for the Study of a Text Corpus},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={218-225},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012316400003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Uncertain Formal Concept Analysis for the Study of a Text Corpus
SN - 978-989-758-680-4
AU - Petiot G.
PY - 2024
SP - 218
EP - 225
DO - 10.5220/0012316400003636
PB - SciTePress