BibTeX

@inProceedings{noble-etal-2024-improving-339991,
	title        = {Improving Word Usage Graphs with Edge Induction},
	abstract     = {This paper investigates edge induction as a method for augmenting Word Usage Graphs, in which word usages (nodes) are connected through scores (edges) representing semantic relatedness. Clustering (densely) annotated WUGs can be used as a way to find senses of a word without relying on traditional word sense annotation. However, annotating all or a majority of pairs of usages is typically infeasible, resulting in sparse graphs and, likely, lower quality senses. In this paper, we ask if filling out WUGs with edges predicted from the human annotated edges improves the eventual clusters. We experiment with edge induction models that use structural features of the existing sparse graph, as well as those that exploit textual (distributional) features of the usages. We find that in both cases, inducing edges prior
to clustering improves correlation with human sense-usage annotation across three different clustering algorithms and languages.},
	booktitle    = {Proceedings of the 5th Workshop on Computational Approaches to Historical Language Change, August 15, 2024, Bangkok, Thailand},
	author       = {Noble, Bill and Periti, Francesco and Tahmasebi, Nina},
	year         = {2024},
	publisher    = {Association for Computational Linguistics},
	address      = {Stroudsburg, PA},
	ISBN         = {979-8-89176-138-4},
}