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Event Entry
What and Who
Language-Aware Truth Assessment of Fact Candidates
Ndapandula Nakashole
Carnegie Mellon University
Talk
AG 5, RG1, MMCI
AG Audience
English
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Date, Time and Location
Monday, 2 June 2014
11:00
60 Minutes
E1 4
433
Saarbrücken
Abstract
In this talk I will introduce FactChecker, a language-aware approach to truth-finding.
FactChecker differs from prior approaches in that it does not rely on iterative peer
voting, instead it leverages language to infer believability of fact candidates. In
particular, FactChecker makes use of linguistic features to detect if a given source
objectively states facts or is speculative and opinionated. To ensure that fact
candidates mentioned in similar sources have similar believability, FactChecker augments objectivity with a co-mention
score to compute the overall believability score of a fact candidate. Our experiments
on various datasets showed that FactChecker yields higher than existing approaches.
FactChecker differs from prior approaches in that it does not rely on iterative peer
voting, instead it leverages language to infer believability of fact candidates. In
particular, FactChecker makes use of linguistic features to detect if a given source
objectively states facts or is speculative and opinionated. To ensure that fact
candidates mentioned in similar sources have similar believability, FactChecker augments objectivity with a co-mention
score to compute the overall believability score of a fact candidate. Our experiments
on various datasets showed that FactChecker yields higher than existing approaches.
Contact
Petra Schaaf
5000
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Petra Schaaf, 05/22/2014 09:46 -- Created document.