Home // International Journal On Advances in Systems and Measurements, volume 15, numbers 3 and 4, 2022 // View article

Semantic Patterns to Structure TimeFrames for Event Ordering Enhancement

Authors:
Nour Matta
Nada Matta
Nicolas Declercq
Agata Marcante

Keywords: Timeframe; Event Extraction; Event Ordering; Natural Language Processing

Abstract:
Event ordering is a field in Event Extraction that deals with the temporality aspect and order of occurrences of events mentioned in a text. Event Ordering is essential because any analysis of causalities and consequences of specific actions or changes of state requires a time evaluation. Standard approaches using machine learning models, with or without inferences, start by identifying events in text and then generate the temporal relationships between them individually. With no consideration of flashbacks, flash-forward, and direct speech temporal aspect, available models lack performance. In this paper, we introduce a novel approach to group events in temporal frames that we refer to as Timeframes. Three types of timeframes will be presented: Publication, Narrative, and Spoken. The purpose of this paper is to highlight the need of this approach, define the different timeframes, introduce their extraction process, evaluate the extraction and compare the event ordering with and without the timeframe approach.

Pages: 121 to 133

Copyright: Copyright (c) to authors, 2022. Used with permission.

Publication date: December 31, 2022

Published in: journal

ISSN: 1942-261x