Search Results for author: Nicola Cancedda

Found 11 papers, 3 papers with code

Multilingual End to End Entity Linking

1 code implementation15 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.

Entity Linking

Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings

no code implementations19 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.

Entity Disambiguation Entity Linking +1

EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing

1 code implementation25 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.

Entity Linking Novel Concepts +1

Multilingual Autoregressive Entity Linking

1 code implementation23 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)

Entity Disambiguation Entity Linking

Reply With: Proactive Recommendation of Email Attachments

no code implementations17 Oct 2017 Christophe Van Gysel, Bhaskar Mitra, Matteo Venanzi, Roy Rosemarin, Grzegorz Kukla, Piotr Grudzien, Nicola Cancedda

Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message.

Weakly-supervised Learning

Task-Driven Linguistic Analysis based on an Underspecified Features Representation

no code implementations LREC 2012 Stasinos Konstantopoulos, Valia Kordoni, Nicola Cancedda, Vangelis Karkaletsis, Dietrich Klakow, Jean-Michel Renders

In this paper we explore a task-driven approach to interfacing NLP components, where language processing is guided by the end-task that each application requires.

Cannot find the paper you are looking for? You can Submit a new open access paper.