TITLE: S2abEL: A Dataset for Entity Linking from Scientific Tables 

ABSTRACT: Entity linking (EL) is the task of linking a textual mention to its
corresponding entry in a knowledge base, and is critical for many
knowledge-intensive NLP applications. When applied to tables in scientific
papers, EL is a step toward large-scale scientific knowledge bases that could
enable advanced scientific question answering and analytics. We present the
first dataset for EL in scientific tables. EL for scientific tables is
especially challenging because scientific knowledge bases can be very
incomplete, and disambiguating table mentions typically requires understanding
the papers's tet in addition to the table. Our dataset, S2abEL, focuses on EL
in machine learning results tables and includes hand-labeled cell types,
attributed sources, and entity links from the PaperswithCode taxonomy for 8,429
cells from 732 tables. We introduce a neural baseline method designed for EL on
scientific tables containing many out-of-knowledge-base mentions, and show that
it significantly outperforms a state-of-the-art generic table EL method. The
best baselines fall below human performance, and our analysis highlights
avenues for improvement.
Entity linking (EL) is the task of linking a textual mention to its
corresponding entry in a knowledge base, and is critical for many
knowledge-intensive NLP applications. When applied to tables in scientific
papers, EL is a step toward large-scale scientific knowledge bases that could
enable advanced scientific question answering and analytics. We present the
first dataset for EL in scientific tables. EL for scientific tables is
especially challenging because scientific knowledge bases can be very
incomplete, and disambiguating table mentions typically requires understanding
the papers's tet in addition to the table. Our dataset, S2abEL, focuses on EL
in machine learning results tables and includes hand-labeled cell types,
attributed sources, and entity links from the PaperswithCode taxonomy for 8,429
cells from 732 tables. We introduce a neural baseline method designed for EL on
scientific tables containing many out-of-knowledge-base mentions, and show that
it significantly outperforms a state-of-the-art generic table EL method. The
best baselines fall below human performance, and our analysis highlights
avenues for improvement.
S2abEL: A Dataset for Entity Linking from Scientific Tables
http://arxiv.org/abs/2305.00366v1