Classifying Words with 3-sort Automata

Tomasz Jastrząb, Frédéric Lardeux, Éric Monfroy

2024

Abstract

Grammatical inference consists in learning a language or a grammar from data. In this paper, we consider a number of models for inferring a non-deterministic finite automaton (NFA) with 3 sorts of states, that must accept some words, and reject some other words from a given sample. We then propose a transformation from this 3-sort NFA into weighted-frequency and probabilistic NFA, and we apply the latter to a classification task. The experimental evaluation of our approach shows that the probabilistic NFAs can be successfully applied for classification tasks on both real-life and superficial benchmark data sets.

Paper Citation

in Harvard Style

Jastrząb T., Lardeux F. and Monfroy É. (2024). Classifying Words with 3-sort Automata. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 1179-1188. DOI: 10.5220/0012454100003636

in Bibtex Style

@conference{icaart24,
author={Tomasz Jastrząb and Frédéric Lardeux and Éric Monfroy},
title={Classifying Words with 3-sort Automata},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={1179-1188},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012454100003636},
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 - Classifying Words with 3-sort Automata
SN - 978-989-758-680-4
AU - Jastrząb T.
AU - Lardeux F.
AU - Monfroy É.
PY - 2024
SP - 1179
EP - 1188
DO - 10.5220/0012454100003636
PB - SciTePress