1 code implementation • 15 Jun 2023 • Mikhail Plekhanov, Nora Kassner, Kashyap Popat, Louis Martin, Simone Merello, Borislav Kozlovskii, Frédéric A. Dreyer, Nicola Cancedda
Entity Linking is one of the most common Natural Language Processing tasks in practical applications, but so far efficient end-to-end solutions with multilingual coverage have been lacking, leading to complex model stacks.
no code implementations • 19 May 2023 • Mattia Atzeni, Mikhail Plekhanov, Frédéric A. Dreyer, Nora Kassner, Simone Merello, Louis Martin, Nicola Cancedda
Inspired by duck typing in programming languages, we propose to define the type of an entity based on the relations that it has with other entities in a knowledge graph.
1 code implementation • 25 May 2022 • Nora Kassner, Fabio Petroni, Mikhail Plekhanov, Sebastian Riedel, Nicola Cancedda
This paper created the Unknown Entity Discovery and Indexing (EDIN) benchmark where unknown entities, that is entities without a description in the knowledge base and labeled mentions, have to be integrated into an existing entity linking system.
1 code implementation • 23 Mar 2021 • Nicola De Cao, Ledell Wu, Kashyap Popat, Mikel Artetxe, Naman Goyal, Mikhail Plekhanov, Luke Zettlemoyer, Nicola Cancedda, Sebastian Riedel, Fabio Petroni
Moreover, in a zero-shot setting on languages with no training data at all, mGENRE treats the target language as a latent variable that is marginalized at prediction time.
Ranked #2 on Entity Disambiguation on Mewsli-9 (using extra training data)