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

TL;DR

TIE-ML is a simplified markup language designed to enhance the efficiency and accuracy of annotating temporal and event information in corpora, facilitating machine learning applications.

Contribution

It introduces a minimalistic schema that simplifies temporal and event annotation, making it easier to use than existing standards like TimeML.

Findings

TIE-ML reduces annotation complexity and improves annotation speed.

It enables lossless mapping from TimeML annotations.

TIE-ML is easier to adopt due to its simplified tag set.

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.