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Communication Dans Un Congrès Année : 2019

Information Nutritional Label and Word Embedding to Estimate Information Check-Worthiness

Résumé

Automatic fact-checking is an important challenge nowadays since anyone can write about anything and spread it in social media, no matter the information quality. In this paper, we revisit the information check-worthiness problem and propose a method that combines the "information nutritional label" features with POS-tags and word-embedding representations. To predict the information check-worthy claim, we train a machine learning model based on these features. We experiment and evaluate the proposed approach on the CheckThat! CLEF 2018 collection. The experimental result shows that our model that combines information nutritional label and word-embedding features outperforms the baselines and the official participants' runs of CheckThat! 2018 challenge.
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Dates et versions

hal-02942338 , version 1 (17-09-2020)

Identifiants

Citer

Cédric Lespagnol, Josiane Mothe, Md Zia Ullah. Information Nutritional Label and Word Embedding to Estimate Information Check-Worthiness. ACM 42nd International SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), Jul 2019, Paris, France. pp.941-944, ⟨10.1145/3331184.3331298⟩. ⟨hal-02942338⟩
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