@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",
    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.",
}

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.
Choi, Hee-Soo Trivedi, Priyansh Constant, Mathieu Fort, Karen Guillaume, Bruno
Beyond Model Performance: Can Link Prediction Enrich French Lexical Graphs?