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- W2767778890 abstract "Fact-checking political discussions has become an essential clog in computational journalism. This task encompasses an important sub-task---identifying the set of statements with 'check-worthy' claims. Previous work has treated this as a simple text classification problem discounting the nuances involved in determining what makes statements check-worthy. We introduce a dataset of political debates from the 2016 US Presidential election campaign annotated using all major fact-checking media outlets and show that there is a need to model conversation context, debate dynamics and implicit world knowledge. We design a multi-classifier system TATHYA, that models latent groupings in data and improves state-of-art systems in detecting check-worthy statements by 19.5% in F1-score on a held-out test set, gaining primarily gaining in Recall." @default.
- W2767778890 created "2017-11-17" @default.
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- W2767778890 date "2017-11-06" @default.
- W2767778890 modified "2023-10-16" @default.
- W2767778890 title "TATHYA" @default.
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- W2767778890 doi "https://doi.org/10.1145/3132847.3133150" @default.
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