Fully Automated Fact Checking Using External Sources

Georgi Karadzhov, Preslav Nakov, Lluís Màrquez, Alberto Barrón-Cedeño, Ivan Koychev


Abstract
Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.
Anthology ID:
R17-1046
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
344–353
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_046
DOI:
10.26615/978-954-452-049-6_046
Bibkey:
Cite (ACL):
Georgi Karadzhov, Preslav Nakov, Lluís Màrquez, Alberto Barrón-Cedeño, and Ivan Koychev. 2017. Fully Automated Fact Checking Using External Sources. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 344–353, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Fully Automated Fact Checking Using External Sources (Karadzhov et al., RANLP 2017)
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PDF:
https://doi.org/10.26615/978-954-452-049-6_046