CLiT: Combining Linking Techniques for EveryoneDownload PDF

Published: 19 Apr 2021, Last Modified: 05 May 2023ESWC2021 P&DReaders: Everyone
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.
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