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				<title level="a" type="main">Automated Fact Checking</title>
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							<persName><forename type="first">Andreas</forename><surname>Vlachos</surname></persName>
							<email>andreas.vlachos@cst.cam.ac.uk</email>
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								<orgName type="department">Department of Computer Science</orgName>
								<orgName type="institution">Technology</orgName>
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								<orgName type="institution">University of Cambridge</orgName>
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						<title level="a" type="main">Automated Fact Checking</title>
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				<application version="0.7.2" ident="GROBID" when="2023-03-24T19:10+0000">
					<desc>GROBID - A machine learning software for extracting information from scholarly documents</desc>
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<div xmlns="http://www.tei-c.org/ns/1.0"><p>Fact checking is the task of verifying a claim against sources such as knowledge bases and text collections. While this task has been of great importance for journalism, it has recently become of interest to the general public as it is one of the weapons against misinformation. In this talk, I will first discuss the task and what should be the expectations from automated methods for it. Following this, I will present our approach for fact checking simple numerical statements which we were able to learn without explicitly labelled data. Then I will describe how we automated part of the manual process of the debunking website emergent.info, which later evolved into the Fake News Challenge with 50 participants. Finally, I will present the Fact Extraction and Verification shared task, which took place in 2018.</p></div>
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