Year: 2025
Type: conference-paper
Abstract: Entity disambiguation (ED) is the task of linking mentions in text to corresponding entries in a knowledge base.Dual Encoders address this by embedding mentions and label candidates in a shared embedding space and applying a similarity metric to predict the correct label.In this work, we focus on evaluating key design decisions for Dual Encoder-based ED, such as its loss function, similarity metric, label verbalization format, and negative sampling strategy.We present the resulting model VERBALIZED, a document-level Dual Encoder model that includes contextual label verbalizations and efficient hard negative sampling.Additionally, we explore an iterative prediction variant that aims to improve the disambiguation of challenging data points.To support our analysis, we first conduct comprehensive ablation experiments on specific design decisions using AIDA-Yago, followed by large-scale, multi-domain evaluation on the ZELDA benchmark.
Authors: Susanna Rücker, Alan Akbik
Institution: Humboldt-Universität zu Berlin
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W4412945340