Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities

no code implementations • AKBC 2021 • John Winn, Matteo Venanzi, Tom Minka, Ivan Korostelev, John Guiver, Elena Pochernina, Pavel Mishkov, Alex Spengler, Denise Wilkins, Sian Lindley, Richard Banks, Sam Webster, Yordan Zaykov

The knowledge discovery process uses a probabilistic program defining the process of generating the data item from a set of unknown typed entities.

Reply With: Proactive Recommendation of Email Attachments

no code implementations • 17 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

Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems

no code implementations • 21 Oct 2015 • Matteo Venanzi, John Guiver, Pushmeet Kohli, Nick Jennings

To relate the quality of a judgment to the time a worker spends on a task, our model assumes that each task is completed within a latent time window within which all workers with a propensity to genuinely attempt the labelling task (i. e., no spammers) are expected to submit their judgments.

Bayesian Inference Entity Linking +1