GREED: Graph Learning Based Relation Extraction with Entity and Dependency Relations

Mohamed Landolsi, Lobna Hlaoua, Lotfi Ben Romdhane

2024

Abstract

A large number of electronic medical documents are generated by specialists, containing valuable information for various medical tasks such as medical prescriptions. Extracting this information from extensive natural language text can be challenging. Named Entity Recognition (NER) and Relation Extraction (RE) are key tasks in clinical information extraction. Systems often rely on machine learning and rule-based techniques. Modern methods involve dependency parsing and graph-based deep learning algorithms. However, the effectiveness of these techniques and certain features is not thoroughly studied. Additionally, it would be advantageous to properly integrate rules with deep learning models. In this paper, we introduce GREED (Graph learning based Relation Extraction with Entity and Dependency relations). GREED is based on graph classification using Graph Convolutional Network (GCN). We transform each sentence into a weighted graph via dependency parsing. Words are represented with features that capture co-occurrence, dependency type, entities, and relation verbs, with focus on the entity pair. Experiments on clinical records (i2b2/VA 2010) show that relevant features efficiently integrated with GCN achieve higher performance.

Paper Citation

in Harvard Style

Landolsi M., Hlaoua L. and Ben Romdhane L. (2024). GREED: Graph Learning Based Relation Extraction with Entity and Dependency Relations. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 360-367. DOI: 10.5220/0012349000003636

in Bibtex Style

@conference{icaart24,
author={Mohamed Landolsi and Lobna Hlaoua and Lotfi Ben Romdhane},
title={GREED: Graph Learning Based Relation Extraction with Entity and Dependency Relations},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={360-367},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012349000003636},
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 - GREED: Graph Learning Based Relation Extraction with Entity and Dependency Relations
SN - 978-989-758-680-4
AU - Landolsi M.
AU - Hlaoua L.
AU - Ben Romdhane L.
PY - 2024
SP - 360
EP - 367
DO - 10.5220/0012349000003636
PB - SciTePress