| 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. |
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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. |
NarrativeTime: Dense Temporal Annotation on a Timeline (2024.lrec-main)
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| Challenge: | e.g. TimeBank contains 1-5% of all possible tlinks, and this information is underspecified in the text. |
| Approach: | They propose a timeline-based framework that achieves full coverage of all possible TLINKs. |
| Outcome: | The proposed framework achieves full coverage of all possible TLINKs in a text. |
Temporal Histories of Epidemic Events (THEE): A Case Study in Temporal Annotation for Public Health (2020.lrec-1)
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| Challenge: | Current EBS estimates the occurrence time of events based on coarse metadata such as document publication time. |
| Approach: | They propose a temporal annotation standard THEE-TimeML and a corpus TheeBank . they document the corpus annotation process and demonstrate the immediate benefit . |
| Outcome: | The proposed standards are based on the existing timeML and the corpus TheeBank . the proposed standards demonstrate the immediate benefit to public health applications . |
Comprehensive Annotation of Various Types of Temporal Information on the Time Axis (L18-1)
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| Challenge: | Existing studies linking event and time information have been conducted to train and evaluate models. |
| Approach: | They propose an annotation scheme that anchors expressions in text to the time axis comprehensively. |
| Outcome: | The proposed scheme can be utilized for integrated information analysis of events, entities and time. |
Structured Interpretation of Temporal Relations (L18-1)
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| Challenge: | Temporal relations between events and time expressions are often modeled in an unstructured manner, resulting in inconsistent and incomplete annotation and computational modeling. |
| Approach: | They propose an annotation approach where events and time expressions form a dependency tree in which each dependency relation corresponds to an instance of temporal anaphora. |
| Outcome: | The proposed approach annotates 235 documents in news and narratives with 48 doubly annotated documents. |
Korean TimeBank Including Relative Temporal Information (L18-1)
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| Challenge: | Temporal information extraction is one of the important research fields in natural language processing. |
| Approach: | They propose a concept of relative temporal information and supplement a Korean annotation language to represent new relative expressions and extend an annotated dataset through the revised language. |
| Outcome: | The proposed language can be used to represent relative temporal information and extend an annotated dataset, Korean TimeBank, through the revised language. |
Errator: a Tool to Help Detect Annotation Errors in the Universal Dependencies Project (L18-1)
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| Challenge: | UD project aims to develop cross-linguistically consistent treebank annotations for a wide array of languages. |
| Approach: | They introduce tools that implement the annotation variation principle to help annotators find and correct errors in UD treebanks. |
| Outcome: | The proposed tools can be used to correct errors in UD treebank annotations. |
Tackling Temporal Questions in Natural Language Interface to Databases (2022.emnlp-industry)
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| Challenge: | Temporal aspect is one of the most challenging areas in Natural Language Interface to Databases (NLIDB). |
| Approach: | They propose a dataset with accompanied databases supporting temporal questions in NLIDB. |
| Outcome: | The proposed dataset helps two models learn and improve in temporal aspect. |
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. |
TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles (2023.emnlp-main)
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| Challenge: | Existing temporal relation extraction models have low inter-annotator agreement due to lack of specificity of annotation guidelines . authors propose a method for annotating all temporal relations, including long-distance ones, which automates the process . |
| Approach: | They propose a new annotation scheme that defines criteria for temporal relations to be annotated . scheme includes events even if they are not expressed as verbs, they argue . |
| Outcome: | The proposed method reduces time and manual effort on the part of annotators. |