Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks

07/03/2019
by   Ben Adler, et al.
0

Factchecking has always been a part of the journalistic process. However with newsroom budgets shrinking it is coming under increasing pressure just as the amount of false information circulating is on the rise. We therefore propose a method to increase the efficiency of the factchecking process, using the latest developments in Natural Language Processing (NLP). This method allows us to compare incoming claims to an existing corpus and return similar, factchecked, claims in a live system-allowing factcheckers to work simultaneously without duplicating their work.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/05/2016

Improving Automated Patent Claim Parsing: Dataset, System, and Experiments

Off-the-shelf natural language processing software performs poorly when ...
research
11/21/2022

Deanthropomorphising NLP: Can a Language Model Be Conscious?

This work is intended as a voice in the discussion over the recent claim...
research
07/27/2019

Towards Effective Rebuttal: Listening Comprehension using Corpus-Wide Claim Mining

Engaging in a live debate requires, among other things, the ability to e...
research
07/24/2017

Extracting Core Claims from Scientific Articles

The number of scientific articles has grown rapidly over the years and t...
research
04/26/2022

Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims

False information has a significant negative influence on individuals as...
research
08/03/2018

Content-driven, unsupervised clustering of news articles through multiscale graph partitioning

The explosion in the amount of news and journalistic content being gener...
research
04/20/2018

Verifying Text Summaries of Relational Data Sets

We present a novel natural language query interface, the AggChecker, aim...

Please sign up or login with your details

Forgot password? Click here to reset