@inproceedings{karadzhov-etal-2017-fully,
title = "Fully Automated Fact Checking Using External Sources",
author = "Karadzhov, Georgi and
Nakov, Preslav and
M{\`a}rquez, Llu{\'\i}s and
Barr{\'o}n-Cede{\~n}o, Alberto and
Koychev, Ivan",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
month = sep,
year = "2017",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://doi.org/10.26615/978-954-452-049-6_046",
doi = "10.26615/978-954-452-049-6_046",
pages = "344--353",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Fully Automated Fact Checking Using External Sources
%A Karadzhov, Georgi
%A Nakov, Preslav
%A Màrquez, Lluís
%A Barrón-Cedeño, Alberto
%A Koychev, Ivan
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F karadzhov-etal-2017-fully
%X 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.
%R 10.26615/978-954-452-049-6_046
%U https://doi.org/10.26615/978-954-452-049-6_046
%P 344-353
Markdown (Informal)
[Fully Automated Fact Checking Using External Sources](https://doi.org/10.26615/978-954-452-049-6_046) (Karadzhov et al., RANLP 2017)
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..