Papers with TimeML
pyTLEX: A Python Library for TimeLine EXtraction (2024.eacl-demo)
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| Challenge: | TimeML is a markup language for temporal information in text. |
| Approach: | pyTLEX is an implementation of the TimeLine EXtraction algorithm . it allows users to parse TimeML annotations, construct TimeML graphs, and execute the algorithm based on TimeML . |
| Outcome: | pyTLEX is an implementation of the TimeLine EXtraction algorithm . it allows users to parse TimeML annotations, construct TimeML graphs, and execute the algorithm to effect complete timeline extraction. |
Holistic Evaluation of Automatic TimeML Annotators (2022.lrec-1)
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| Challenge: | TimeML is an annotation scheme for representing temporal information in texts. |
| Approach: | They propose to combine eight metrics for holistic evaluation of TimeML graphs. |
| Outcome: | The proposed system produces graphs with 1/3 of the time indeterminacy and 1/3 of gold standard . the proposed system is compared with four other systems and is a good fit for the proposed task. |
Temporal Relations Annotation and Extrapolation Based on Semi-intervals and Boundig Relations (2020.coling-main)
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| Challenge: | Existing methods for temporal relations annotation and management are not widely used because they are too complex from the computational perspective. |
| Approach: | They propose a system for the annotation and management of temporal relations that combines the richness and expressiveness of Freksa’s approach with the simplicity of Allen’s notation. |
| Outcome: | The proposed system achieves more agreeable representations of temporal relations without increasing the complexity of the labeling process. |
A Comprehensive Evaluation and Correction of the TimeBank Corpus (2022.lrec-1)
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| Challenge: | TimeML is an annotation scheme for capturing temporal information in text. |
| Approach: | They propose to use TimeML to validate TimeML and provide a rich dataset of events, temporal expressions, and temporal relationships for training and testing temporal analysis systems. |
| Outcome: | The proposed methods detect and correct errors in the TimeML corpus and provide a reference corpus for training and testing temporal analysis systems. |