Empirical Evaluation of Pretraining Strategies for Supervised Entity LinkingDownload PDF

14 Feb 2020 (modified: 27 May 2020)AKBC 2020 Conference Blind SubmissionReaders: Everyone
  • Keywords: Entity linking, Pre-training, Wikification
  • TL;DR: We achieve state of the art on CoNLL and TAC-KBP 2010 with a four layer transformer
  • Subject Areas: Information Extraction, Machine Learning
  • Archival Status: Archival
  • Abstract: In this work, we present an entity linking model which combines a Transformer architecture with large scale pretraining from Wikipedia links. Our model achieves the state-of-the-art on two commonly used entity linking datasets: 96.7% on CoNLL and 94.9% on TAC-KBP. We present detailed analyses to understand what design choices are important for entity linking, including choices of negative entity candidates, Transformer architecture, and input perturbations. Lastly, we present promising results on more challenging settings such as end-to-end entity linking and entity linking without in-domain training data
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