Leveraging Contextual Information for Effective Entity Salience Detection

no code implementations • 14 Sep 2023 • Rajarshi Bhowmik, Marco Ponza, Atharva Tendle, Anant Gupta, Rebecca Jiang, Xingyu Lu, Qian Zhao, Daniel Preotiuc-Pietro

In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document.

Benchmarking Feature Engineering +1

Facts That Matter

1 code implementation • EMNLP 2018 • Marco Ponza, Luciano del Corro, Gerhard Weikum

This work introduces fact salience: The task of generating a machine-readable representation of the most prominent information in a text document as a set of facts.

Clustering Entity Linking +4

WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking

no code implementations • 10 May 2018 • Paolo Cifariello, Paolo Ferragina, Marco Ponza

Every node is also labeled with a relevance score which models the pertinence of the corresponding entity to author's expertise, and is computed by means of a proper random-walk calculation over that graph; and with a latent vector representation which is learned via entity and other kinds of structural embeddings derived from Wikipedia.

Entity Linking Language Modelling

SWAT: A System for Detecting Salient Wikipedia Entities in Texts

no code implementations • 10 Apr 2018 • Marco Ponza, Paolo Ferragina, Francesco Piccinno

We study the problem of entity salience by proposing the design and implementation of SWAT, a system that identifies the salient Wikipedia entities occurring in an input document.