Skip to Main content Skip to Navigation
New interface
Conference papers

Uncertainty Detection in Historical Databases

Abstract : Historians analyze information from diverse and heterogeneous sources to verify hypotheses and/or to propose new ones. Central to any historical project is the concept of uncertainty, reflecting a lack of confidence. This may limit the scope of the hypotheses formulated. Uncertainty encompasses a variety of aspects including ambiguity, incompleteness, vagueness, randomness, and inconsistency. These aspects cannot be easily detected automatically in plain-text documents. The objective of this article is to propose a process for detecting uncertainty, combining dictionary-based approaches, and pattern identification. The process is validated through experiments conducted on a real historical data set.
Document type :
Conference papers
Complete list of metadata

https://hal-cnam.archives-ouvertes.fr/hal-03843641
Contributor : Marie-Liesse Bertram Connect in order to contact the contributor
Submitted on : Tuesday, November 8, 2022 - 11:56:52 AM
Last modification on : Thursday, November 10, 2022 - 4:39:52 AM

Identifiers

Collections

Citation

Wissam Mammar Kouadri, Jacky Akoka, Isabelle Comyn-Wattiau, Cedric Du Mouza. Uncertainty Detection in Historical Databases. 27th International Conference on Applications of Natural Language to Information Systems, Jun 2022, Valencia, Spain. pp.73-85, ⟨10.1007/978-3-031-08473-7_7⟩. ⟨hal-03843641⟩

Share

Metrics

Record views

0