Abstract
Word embeddings revolutionised natural language processing by effectively representing words as dense vectors. Although many datasets exist to evaluate English embeddings, few cater to Dutch. We developed a Dutch variant of the SimLex-999 word similarity dataset by gathering similarity judgements from 235 native Dutch speakers. Subsequently, we evaluated two popular Dutch language models, Bertje and RobBERT, finding that Bertje showed superior alignment with human semantic similarity judgments compared to RobBERT. This study provides the first intrinsic Dutch word embedding evaluation dataset, which enables accurate assessment of these embeddings and fosters the development of effective Dutch language models.
| Original language | English |
|---|---|
| Title of host publication | 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings |
| Editors | Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue |
| Publisher | European Language Resources Association (ELRA) |
| Pages | 14832-14845 |
| Number of pages | 14 |
| ISBN (Electronic) | 9782493814104 |
| Publication status | Published - May 2024 |
| Event | Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024 - Hybrid, Torino, Italy Duration: 20 May 2024 → 25 May 2024 |
Publication series
| Name | 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings |
|---|
Conference
| Conference | Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024 |
|---|---|
| Country/Territory | Italy |
| City | Hybrid, Torino |
| Period | 20/05/24 → 25/05/24 |
Bibliographical note
Publisher Copyright:© 2024 ELRA Language Resource Association: CC BY-NC 4.0.