From TagME to WAT: a new entity annotator

216 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

End-to-End Neural Entity Linking (2018, unknown): “WAT (Piccinno and Ferragina, 2014), Dbpedia Spotlight (Daiber et al.”
Robustness Gym: Unifying the NLP Evaluation Landscape (2021, unknown): “…a self-supervised system for NEL, (ii) R EL [van Hulst et al., 2020], a system that combines existing state-of-the-art approaches, (iii) W AT [Piccinno and Ferragina, 2014] an extension of the TAGME [Ferragina and Scaiella, 2010] linker, and (iv) P OP , our simple heuristic baseline that…”
Latent Relation Language Models (2019, background): “Moreover, the posterior probabilities of Z can also be considered as entity links, which are of interest in their own right in the information extraction field (Ceccarelli et al., 2013; Piccinno and Ferragina, 2014; Ganea and Hofmann, 2017).”
Entity Disambiguation with Web Links (2015, background): “Analysis shows that these can have a large effect on system performance (Hachey et al., 2013; Piccinno and Ferragina, 2014).”
GERBIL - Benchmarking Named Entity Recognition and Linking consistently (2017, background): “While manifold annotation tools have been developed over recent years to address (some of) the subtasks related to the extraction of structured data from unstructured data [21,27,39,41,43,49,53,60,63], the provision of comparable results for these tools remains a tedious problem.”