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Semantic Patterns to Structure TimeFrames in Text

Authors:
Nour Matta
Nada Matta
Nicolas Declercq
Agata Marcante

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

Abstract:
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.

Pages: 16 to 23

Copyright: Copyright (c) IARIA, 2022

Publication date: May 22, 2022

Published in: conference

ISSN: 2308-4065

ISBN: 978-1-61208-977-5

Location: Venice, Italy

Dates: from May 22, 2022 to May 26, 2022