More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages
Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte Im Walde, Nina Tahmasebi
Abstract
Word Usage Graphs (WUGs) represent human semantic proximity judgments for pairs of word uses in a weighted graph, which can be clustered to infer word sense clusters from simple pairwise word use judgments, avoiding the need for word sense definitions. SemEval-2020 Task 1 provided the first and to date largest manually annotated, diachronic WUG dataset. In this paper, we check the robustness and correctness of the annotations by continuing the SemEval annotation algorithm for two more rounds and comparing against an established annotation paradigm. Further, we test the reproducibility by resampling a new, smaller set of word uses from the SemEval source corpora and annotating them. Our work contributes to a better understanding of the problems and opportunities of the WUG annotation paradigm and points to future improvements.
BibTeX
@inproceedings{schlechtweg-etal-2024-dwugs,
title = "More {DWUG}s: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages",
author = "Schlechtweg, Dominik and
Cassotti, Pierluigi and
Noble, Bill and
Alfter, David and
Schulte Im Walde, Sabine and
Tahmasebi, Nina",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.796/",
doi = "10.18653/v1/2024.emnlp-main.796",
pages = "14379--14393"
}