Temporal Information and Event Markup Language: TIE-ML Markup Process and Schema Version 1.0
This work addresses the problem of cumbersome annotation processes for researchers and practitioners in natural language processing, though it is incremental as it builds on prior markup languages.
The paper introduces TIE-ML, a markup language and schema designed to simplify temporal and event annotation in corpora for machine learning model training, resulting in improved productivity and accuracy by reducing tag complexity compared to existing standards like TimeML.
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.