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Temporal Information and Event Markup Language: TIE-ML Markup Process and Schema Version 1.0

Authors:
Damir Cavar
Billy Dickson
Ali Aljubailan
Soyoung Kim

Keywords: TIE-ML, Events, Time, Corpora, Machine Learning.

Abstract:
Temporal Information and Event Markup Language (TIE-ML) is a markup strategy and annotation schema to improve the productivity and accuracy of temporal and event related annotation of corpora to facilitate machine learning based model training. For the annotation of events, temporal sequencing, and durations, it is significantly simpler by providing an extremely reduced tag set for just temporal relations and event enumeration. In comparison to other standards, as for example the Time Markup Language (TimeML), it is much easier to use by dropping sophisticated formalisms, theoretical concepts, and annotation approaches. Annotations of corpora using TimeML can be mapped to TIE-ML with a loss, and TIE-ML annotations can be fully mapped to TimeML with certain under-specification.

Pages: 29 to 36

Copyright: Copyright (c) IARIA, 2021

Publication date: October 3, 2021

Published in: conference

ISSN: 2308-4510

ISBN: 978-1-61208-888-4

Location: Barcelona, Spain

Dates: from October 3, 2021 to October 7, 2021