Entity Matching (EM) aims to determine whether records in two datasets refer to the same
real-world entity. Existing work often uses Pre-trained Language Models (PLMs) for feature …

Entity resolution (ER) approaches typically consist of a blocker and a matcher. They share
the same goal and cooperate in different roles: the blocker first quickly removes obvious non …

Data collection and labeling are critical bottlenecks in the deployment of machine learning
applications. With the increasing complexity and diversity of applications, the need for …

Entity matching (EM) is a critical task in data integration, aiming to identify records across
different datasets that refer to the same real-world entities. Traditional methods often rely on …

We introduce Onion Universe Algorithm (OUA), a novel classification method in ensemble
learning. In particular, we show its applicability as a label model for weakly supervised …

In recent years, machine learning, particularly deep learning models, have seen significant
growth and made impact in various real-world applications. These models bypass the need …

With the rapid growth of e-commerce, the challenge of item matching has become
increasingly significant. This research focuses on exploring contemporary approaches to …

We introduce RACH-Space, a novel classification method in ensemble learning. In
particular, we show its applicability as a label model for weakly supervised learning. RACH …