activity
20182021
most citedFEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information

52 citations · 52 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL202152 cited

FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information

Rami Aly, Zhijiang Guo, Michael Schlichtkrull +5

Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation…

cs.CL2021

An Explanatory Query-Based Framework for Exploring Academic Expertise

Oana Cocarascu, Andrew McLean, Paul French +1

The success of research institutions heavily relies upon identifying the right researchers "for the job": researchers may need to identify appropriate collaborators, often from acr…

cs.CL2021

Automatic Product Ontology Extraction from Textual Reviews

Joel Oksanen, Oana Cocarascu, Francesca Toni

Ontologies have proven beneficial in different settings that make use of textual reviews. However, manually constructing ontologies is a laborious and time-consuming process in nee…

cs.CL2020

A Dataset Independent Set of Baselines for Relation Prediction in Argument Mining

Oana Cocarascu, Elena Cabrio, Serena Villata +1

Argument Mining is the research area which aims at extracting argument components and predicting argumentative relations (i.e.,support and attack) from text. In particular, numerou…

cs.CL2018

The Fact Extraction and VERification (FEVER) Shared Task

James Thorne, Andreas Vlachos, Oana Cocarascu +2

We present the results of the first Fact Extraction and VERification (FEVER) Shared Task. The task challenged participants to classify whether human-written factoid claims could be…