Semantic Patterns to Structure TimeFrames in Text’ - Université de technologie de Troyes Access content directly
Conference Papers Year : 2022

Semantic Patterns to Structure TimeFrames in Text’


Event ordering is a very important task in the event extraction field since any analysis of the causality and impacts of a specific action or a change requires consideration of temporality and ordering. Many pattern-based approaches or machine learning approaches work on identifying the events in the text and creating relationships between them. In this paper, we present a novel approach based on timeframes, that will enable distinction between multiple timeframes in a text, when available, and grouping events within these timeframes.
No file

Dates and versions

hal-03866615 , version 1 (22-11-2022)


  • HAL Id : hal-03866615 , version 1


Nour Matta, Nada Matta, Nicolas Declercq, Agata Marcante. Semantic Patterns to Structure TimeFrames in Text’. INTELLI 2022 : The Eleventh International Conference on Intelligent Systems and Applications, May 2022, Venise, Italy. pp.16-23. ⟨hal-03866615⟩
45 View
0 Download


Gmail Facebook X LinkedIn More