S2abEL: A Dataset for Entity Linking from Scientific Tables

• URL: http://arxiv.org/abs/2305.00366v1
• Date: Sun, 30 Apr 2023 02:07:22 GMT
• Title: S2abEL: A Dataset for Entity Linking from Scientific Tables
• Authors: Yuze Lou, Bailey Kuehl, Erin Bransom, Sergey Feldman, Aakanksha Naik, Doug Downey
• Abstract summary: We present the first dataset for entity linking in scientific tables. Our dataset, S2abEL, focuses on EL in machine learning results tables. We introduce a neural baseline method designed for EL on scientific tables containing many out-of-knowledge-base mentions.
• Score: 15.300960829210164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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