Abstract
Entity resolution, the process of discerning whether multiple data refer to the same real-world entity, is crucial
across various domains, including education. Its quality assessment is vital due to the extensive practical
applications in fields such as analytics, personalized learning or academic integrity. With Python emerging as
the predominant programming language in these areas, this paper attempts to fill in a gap when evaluating the
qualitative performance of entity resolution tasks by proposing a novel consistent library dedicated exclusively
for this purpose. This library not only facilitates precise evaluation but also aligns with contemporary research
and application trends, making it a significant tool for practitioners and researchers in the field.
Citare
@Inproceedings{Olar2024PyResolveMetricsAS,
author = {Andrei Olar and L. Dioşan},
booktitle = {International Conference on Computer Supported Education},
title = {PyResolveMetrics: A Standards-Compliant and Efficient Approach to Entity Resolution Metrics},
year = {2024}
}