CLiT: Combining Linking Techniques for Everyone

Kristian Noullet, Samuel Printz, Michael Färber

Keywords: entity linking, meta learning, reproducibility, nlp, semantic web

TL;DR: A framework for combining linking techniques in fancy ways and pleasing EL researchers!

Abstract:

While the path in the field of Entity Linking (EL) has been long and brought forth a plethora of approaches over the years, many of these are exceedingly difficult to execute for purposes of detailed analysis.
In many cases, implementations are available, but far from being a plug-and-play experience.
We present Combining Linking Techniques (CLiT), a framework with the purpose of executing singular linking techniques and complex combinations thereof, with a higher degree of reusability, reproducibility and comparability of existing systems in mind.
Furthermore, we introduce protocols for the exchange of sub-pipeline-level information with existing and novel systems for heightened out-of-the-box compatibility.
Among others, our framework may be used to consolidate multiple systems in combination with meta learning approaches and increase support for backwards compatibility of existing benchmark annotation systems.