Auswahl der wissenschaftlichen Literatur zum Thema „Explainable fact checking“
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Zeitschriftenartikel zum Thema "Explainable fact checking"
Zeng, Fengzhu, und Wei Gao. „JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims“. Transactions of the Association for Computational Linguistics 12 (2024): 334–54. http://dx.doi.org/10.1162/tacl_a_00649.
Der volle Inhalt der QuelleAugenstein, Isabelle. „Habilitation Abstract: Towards Explainable Fact Checking“. KI - Künstliche Intelligenz, 13.09.2022. http://dx.doi.org/10.1007/s13218-022-00774-6.
Der volle Inhalt der QuelleLinder, Rhema, Sina Mohseni, Fan Yang, Shiva K. Pentyala, Eric D. Ragan und Xia Ben Hu. „How level of explanation detail affects human performance in interpretable intelligent systems: A study on explainable fact checking“. Applied AI Letters 2, Nr. 4 (26.11.2021). http://dx.doi.org/10.1002/ail2.49.
Der volle Inhalt der QuelleDissertationen zum Thema "Explainable fact checking"
Ahmadi, Naser. „A framework for the continuous curation of a knowledge base system“. Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS320.
Der volle Inhalt der QuelleEntity-centric knowledge graphs (KGs) are becoming increasingly popular for gathering information about entities. The schemas of KGs are semantically rich, with many different types and predicates to define the entities and their relationships. These KGs contain knowledge that requires understanding of the KG’s structure and patterns to be exploited. Their rich data structure can express entities with semantic types and relationships, oftentimes domain-specific, that must be made explicit and understood to get the most out of the data. Although different applications can benefit from such rich structure, this comes at a price. A significant challenge with KGs is the quality of their data. Without high-quality data, the applications cannot use the KG. However, as a result of the automatic creation and update of KGs, there are a lot of noisy and inconsistent data in them and, because of the large number of triples in a KG, manual validation is impossible. In this thesis, we present different tools that can be utilized in the process of continuous creation and curation of KGs. We first present an approach designed to create a KG in the accounting field by matching entities. We then introduce methods for the continuous curation of KGs. We present an algorithm for conditional rule mining and apply it on large graphs. Next, we describe RuleHub, an extensible corpus of rules for public KGs which provides functionalities for the archival and the retrieval of rules. We also report methods for using logical rules in two different applications: teaching soft rules to pre-trained language models (RuleBert) and explainable fact checking (ExpClaim)
„Explainable Fact Checking by Combining Automated Rule Discovery with Probabilistic Answer Set Programming“. Master's thesis, 2018. http://hdl.handle.net/2286/R.I.50443.
Der volle Inhalt der QuelleDissertation/Thesis
Masters Thesis Computer Science 2018
Buchteile zum Thema "Explainable fact checking"
Atanasova, Pepa. „Generating Fact Checking Explanations“. In Accountable and Explainable Methods for Complex Reasoning over Text, 83–103. Cham: Springer Nature Switzerland, 2020. http://dx.doi.org/10.1007/978-3-031-51518-7_4.
Der volle Inhalt der QuelleAtanasova, Pepa. „Fact Checking with Insufficient Evidence“. In Accountable and Explainable Methods for Complex Reasoning over Text, 39–64. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-51518-7_2.
Der volle Inhalt der QuelleAtanasova, Pepa. „Multi-Hop Fact Checking of Political Claims“. In Accountable and Explainable Methods for Complex Reasoning over Text, 131–51. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-51518-7_6.
Der volle Inhalt der QuelleAtanasova, Pepa. „Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing“. In Accountable and Explainable Methods for Complex Reasoning over Text, 105–30. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-51518-7_5.
Der volle Inhalt der QuelleAlthabiti, Saud, Mohammad Ammar Alsalka und Eric Atwell. „Generative AI for Explainable Automated Fact Checking on the FactEx: A New Benchmark Dataset“. In Disinformation in Open Online Media, 1–13. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47896-3_1.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Explainable fact checking"
Kotonya, Neema, und Francesca Toni. „Explainable Automated Fact-Checking: A Survey“. In Proceedings of the 28th International Conference on Computational Linguistics. Stroudsburg, PA, USA: International Committee on Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.coling-main.474.
Der volle Inhalt der QuelleKotonya, Neema, und Francesca Toni. „Explainable Automated Fact-Checking: A Survey“. In Proceedings of the 28th International Conference on Computational Linguistics. Stroudsburg, PA, USA: International Committee on Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.coling-main.474.
Der volle Inhalt der QuelleYang, Jing, Didier Vega-Oliveros, Tais Seibt und Anderson Rocha. „Explainable Fact-Checking Through Question Answering“. In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747214.
Der volle Inhalt der QuelleSamarinas, Chris, Wynne Hsu und Mong Li Lee. „Improving Evidence Retrieval for Automated Explainable Fact-Checking“. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.naacl-demos.10.
Der volle Inhalt der QuelleAhmadi, Naser, Joohyung Lee, Paolo Papotti und Mohammed Saeed. „Explainable Fact Checking with Probabilistic Answer Set Programming“. In Conference for Truth and Trust Online 2019. TTO Conference Ltd., 2019. http://dx.doi.org/10.36370/tto.2019.15.
Der volle Inhalt der QuelleKotonya, Neema, und Francesca Toni. „Explainable Automated Fact-Checking for Public Health Claims“. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.emnlp-main.623.
Der volle Inhalt der QuelleNikopensius, Gustav, Mohit Mayank, Orchid Chetia Phukan und Rajesh Sharma. „Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking“. In ASONAM '23: International Conference on Advances in Social Networks Analysis and Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3625007.3627593.
Der volle Inhalt der QuelleLourenco, Vitor, und Aline Paes. „A Modality-level Explainable Framework for Misinformation Checking in Social Networks“. In LatinX in AI at Neural Information Processing Systems Conference 2022. Journal of LatinX in AI Research, 2022. http://dx.doi.org/10.52591/lxai202211283.
Der volle Inhalt der QuelleAlthabiti, Saud, Mohammad Ammar Alsalka und Eric Atwell. „TA’KEED the First Generative Fact-Checking System for Arabic Claims“. In 11th International Conference on Artificial Intelligence and Applications. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140103.
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