Beyond Model Performance: Can Link Prediction Enrich French Lexical Graphs?
Hee-Soo Choi, Priyansh Trivedi, Mathieu Constant, Karen Fort, Bruno Guillaume
Abstract
This paper presents a resource-centric study of link prediction approaches over French lexical-semantic graphs. Our study incorporates two graphs, RezoJDM16k and RL-fr, and we evaluated seven link prediction models, with CompGCN-ConvE emerging as the best performer. We also conducted a qualitative analysis of the predictions using manual annotations. Based on this, we found that predictions with higher confidence scores were more valid for inclusion. Our findings highlight different benefits for the dense graph compared to the sparser graph RL-fr. While the addition of new triples to RezoJDM16k offers limited advantages, RL-fr can benefit substantially from our approach.
BibTeX
@inproceedings{choi-etal-2024-beyond,
title = "Beyond Model Performance: Can Link Prediction Enrich {F}rench Lexical Graphs?",
author = "Choi, Hee-Soo and
Trivedi, Priyansh and
Constant, Mathieu and
Fort, Karen and
Guillaume, Bruno",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.208/",
pages = "2329--2341"
}