Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing

no code implementations • LREC 2020 • Sora Lim, Adam Jatowt, Michael F{\"a}rber, Masatoshi Yoshikawa

In this paper, we propose a novel news bias dataset which facilitates the development and evaluation of approaches for detecting subtle bias in news articles and for understanding the characteristics of biased sentences.

Bias Detection Fake News Detection +1

KORE 50\^DYWC: An Evaluation Data Set for Entity Linking Based on DBpedia, YAGO, Wikidata, and Crunchbase

no code implementations • LREC 2020 • Kristian Noullet, Rico Mix, Michael F{\"a}rber

Therefore, we have extended a widely-used gold standard data set, KORE 50, to not only accommodate NERD tasks for DBpedia, but also for YAGO, Wikidata and Crunchbase.

Entity Linking Knowledge Graphs +3

Team Peter Brinkmann at SemEval-2019 Task 4: Detecting Biased News Articles Using Convolutional Neural Networks

1 code implementation • SEMEVAL 2019 • Michael F{\"a}rber, Agon Qurdina, Lule Ahmedi

In this paper, we present an approach for classifying news articles as biased (i. e., hyperpartisan) or unbiased, based on a convolutional neural network.

A High-Quality Gold Standard for Citation-based Tasks

no code implementations • LREC 2018 • Michael F{\"a}rber, Alex Thiemann, er, Adam Jatowt

Entity Linking Entity Resolution +1

xLiD-Lexica: Cross-lingual Linked Data Lexica

no code implementations • LREC 2014 • Lei Zhang, Michael F{\"a}rber, Achim Rettinger

In this paper, we introduce our cross-lingual linked data lexica, called xLiD-Lexica, which are constructed by exploiting the multilingual Wikipedia and linked data resources from Linked Open Data (LOD).

Cross-Lingual Entity Linking Entity Linking +3